From 230fba1f9bf0b1f582a11a161216616ce389499e Mon Sep 17 00:00:00 2001 From: Giabao Tonthat <45995299+Diluent67@users.noreply.github.com> Date: Wed, 4 Oct 2023 22:03:57 +0100 Subject: [PATCH 01/64] Add or update the Azure App Service build and deployment workflow config --- .github/workflows/main_battlesnake-g.yml | 63 ++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 .github/workflows/main_battlesnake-g.yml diff --git a/.github/workflows/main_battlesnake-g.yml b/.github/workflows/main_battlesnake-g.yml new file mode 100644 index 00000000..d191852e --- /dev/null +++ b/.github/workflows/main_battlesnake-g.yml @@ -0,0 +1,63 @@ +# Docs for the Azure Web Apps Deploy action: https://github.com/Azure/webapps-deploy +# More GitHub Actions for Azure: https://github.com/Azure/actions +# More info on Python, GitHub Actions, and Azure App Service: https://aka.ms/python-webapps-actions + +name: Build and deploy Python app to Azure Web App - battlesnake-g + +on: + push: + branches: + - main + workflow_dispatch: + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Set up Python version + uses: actions/setup-python@v1 + with: + python-version: '3.10' + + - name: Create and start virtual environment + run: | + python -m venv venv + source venv/bin/activate + + - name: Install dependencies + run: pip install -r requirements.txt + + # Optional: Add step to run tests here (PyTest, Django test suites, etc.) + + - name: Upload artifact for deployment jobs + uses: actions/upload-artifact@v2 + with: + name: python-app + path: | + . + !venv/ + + deploy: + runs-on: ubuntu-latest + needs: build + environment: + name: 'Production' + url: ${{ steps.deploy-to-webapp.outputs.webapp-url }} + + steps: + - name: Download artifact from build job + uses: actions/download-artifact@v2 + with: + name: python-app + path: . + + - name: 'Deploy to Azure Web App' + uses: azure/webapps-deploy@v2 + id: deploy-to-webapp + with: + app-name: 'battlesnake-g' + slot-name: 'Production' + publish-profile: ${{ secrets.AZUREAPPSERVICE_PUBLISHPROFILE_332F8044B4DD4F86877E31EF610C4744 }} From 711fb677aa1ae4f2657beb041326243308d8e863 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 4 Oct 2023 18:52:12 +0100 Subject: [PATCH 02/64] New --- helper_battlesnake.py | 225 ++++++++++++++++++++++++++++++++++++++++++ main.py | 90 +++++++++-------- test.py | 97 ++++++++++++++++++ test_flood_fill.py | 135 +++++++++++++++++++++++++ 4 files changed, 506 insertions(+), 41 deletions(-) create mode 100644 helper_battlesnake.py create mode 100644 test.py create mode 100644 test_flood_fill.py diff --git a/helper_battlesnake.py b/helper_battlesnake.py new file mode 100644 index 00000000..579159d5 --- /dev/null +++ b/helper_battlesnake.py @@ -0,0 +1,225 @@ +import numpy as np +import typing + + +def look_ahead(head_loc, direction): + """ + Given the snake's coordinates and a possible move, return the snake's new coordinates if it were headed that way + """ + snake_head = head_loc.copy() + if direction == "up": + snake_head["y"] += 1 + if direction == "down": + snake_head["y"] -= 1 + if direction == "left": + snake_head["x"] -= 1 + if direction == "right": + snake_head["x"] += 1 + return snake_head + + +def snake_compass(head_loc: typing.Dict, next_loc: typing.Dict): + """ + Given the snake's head and an adjacent square, return either left/right/up/down + """ + if head_loc["x"] + 1 == next_loc["x"] and head_loc["y"] == next_loc["y"]: + return "right" + if head_loc["x"] - 1 == next_loc["x"] and head_loc["y"] == next_loc["y"]: + return "left" + if head_loc["x"] == next_loc["x"] and head_loc["y"] + 1 == next_loc["y"]: + return "up" + if head_loc["x"] == next_loc["x"] and head_loc["y"] - 1 == next_loc["y"]: + return "down" + +def manhattan_distance(start: typing.Dict, end: typing.Dict): + """ + Return the Manhattan distance for two positions + """ + start = (start["x"], start["y"]) + end = (end["x"], end["y"]) + return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) + + +def refresh_safe_moves(move_dict): + """ + Return any remaining safe moves + """ + safe_moves = [] + for move, isSafe in move_dict.items(): + if isSafe: + safe_moves.append(move) + return safe_moves + + +def obvious_moves(game_state: typing.Dict, my_head: typing.Dict, my_neck=None, risk_averse=True): + """ + Update the move dictionary and mark unsafe directions as False + """ + is_move_safe = {"up": True, "down": True, "left": True, "right": True} + + if my_neck is None: + my_neck = game_state["you"]["body"][1] # Coordinates of your "neck" + # print(f"Head location: {str(my_head)}") + + if my_neck["x"] < my_head["x"]: # Neck is left of head, don't move left + is_move_safe["left"] = False + # print("Neck is left of head, don't move left") + elif my_neck["x"] > my_head["x"]: # Neck is right of head, don't move right + is_move_safe["right"] = False + # print("Neck is right of head, don't move right") + elif my_neck["y"] < my_head["y"]: # Neck is below head, don't move down + is_move_safe["down"] = False + # print("Neck is below head, don't move down") + elif my_neck["y"] > my_head["y"]: # Neck is above head, don't move up + is_move_safe["up"] = False + # print("Neck is above head, don't move up") + + # Step 1 - Prevent your Battlesnake from moving out of bounds + board_width = game_state['board']['width'] + board_height = game_state['board']['height'] + + if my_head["x"] == 0: + is_move_safe["left"] = False + # print("Out of bounds, can't move left") + if my_head["x"] + 1 == board_height: + is_move_safe["right"] = False + # print("Out of bounds, can't move right") + if my_head["y"] == 0: + is_move_safe["down"] = False + # print("Out of bounds, can't move down") + if my_head["y"] + 1 == board_width: + is_move_safe["up"] = False + # print("Out of bounds, can't move up") + + # Step 2 - Prevent your Battlesnake from colliding with itself + my_body = game_state['you']['body'] + for direction in refresh_safe_moves(is_move_safe): + possible_move = look_ahead(my_head, direction) + if possible_move in my_body: + is_move_safe[direction] = False + # print(f"Body in the way, can't move {direction}") + # print(f"Headed towards: {str(possible_move)}") + # print(f"Body: {str(my_body)}") + + # Step 3 - Prevent your Battlesnake from colliding with other Battlesnakes + opponents = game_state['board']['snakes'] + for opponent in opponents: + if opponent["id"] == game_state['you']["id"]: # Ignore yourself + continue + + opponent_locs = opponent["body"] + opponent_head = opponent_locs[0] + for direction in ["up", "down", "left", "right"]: + possible_move = look_ahead(my_head, direction) + # Avoid any possible encounters with any future opponent move + if risk_averse: + opponent_move = [look_ahead(opponent_head, direction) for direction in ["up", "down", "left", "right"]] + if possible_move in opponent_move: + is_move_safe[direction] = False + # Avoid running into the opponent snake + if possible_move in opponent_locs: + is_move_safe[direction] = False + # print(f"Opponent snake in the way, can't move {direction}") + # print(f"Headed towards: {str(possible_move)}") + # print(f"Opponent: {str(opponent_locs)}") + + return refresh_safe_moves(is_move_safe) + + +class Node: + def __init__(self, location, parent=None, f=0, g=0, h=0): + self.location = location + self.neighbour_nodes = [] + self.parent = parent + self.f = f # The cost from the start node to the goal node + self.g = g # The cost from the start node to the current node + self.h = h # The cost from the current node to the goal node using a heuristic + + def __repr__(self): + return str(self.location) + + +def a_star_search(game_state, head_loc, food_loc): + """ + Implement the A* search algorithm to find the shortest path to food + """ + open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet + closed_list = [] # Contains the nodes which are examined + timer = 0 + + while len(open_list) > 0: + if timer > 200: + print("Past 200") + return None, None + + current_node = min(open_list, key = lambda node: node.f) # The node with the smallest f value + open_list.remove(current_node) # Remove from open list + + if current_node.location == food_loc: # Found the food! Stop search + traceback = [food_loc] + pointer = current_node + while pointer.parent is not None: + traceback.append(pointer.parent.location) + pointer = pointer.parent + return traceback, current_node.g + + # Identify all possible nodes to move to + neighbour_nodes = [] + possible_moves = obvious_moves( + game_state, + my_head=current_node.location, + my_neck=current_node.parent.location if current_node.parent is not None else None, + risk_averse=False + ) + + for possible_move in possible_moves: + possible_node = Node(look_ahead(current_node.location, possible_move), parent=current_node) + neighbour_nodes.append(possible_node) + + for possible_node in neighbour_nodes: # For each possible new node + if possible_node.location in closed_list: # Skip if the node is in the closed list + continue + + # Compute f value for the neighbour node + possible_node.g = current_node.g + 1 + possible_node.h = manhattan_distance(current_node.location, food_loc) + possible_node.f = possible_node.g + possible_node.h + + # If the node's location is also in the open list but has a worse g value, ignore since we can do better + for open_node in open_list: + if possible_node.location == open_node.location and possible_node.g > open_node.g: + break + + # Otherwise, this is the best we can do so far - add to open list + open_list.append(possible_node) + + closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours + timer += 1 + + return None, None + +def flood_fill(game_state, next_square): + board_width = game_state['board']['width'] + board_height = game_state['board']['height'] + board = np.zeros((board_width, board_height)) + # Make an (l x w) array and simulate the board + for pixel in game_state["you"]["body"]: + board[pixel["x"], pixel["y"]] = 1 + for snake in game_state["board"]["snakes"]: + body_pixels = snake["body"] + for pixel in body_pixels: + board[pixel["x"], pixel["y"]] = 1 + + def fill(x, y): + if board[x][y] == 1: + return + if board[x][y] == 2: + return + board[x][y] = 2 + neighbours = [(x-1,y),(x+1,y),(x,y-1),(x,y+1)] + for n in neighbours: + if 0 <= n[0] < board_width and 0 <= n[1] < board_height: + fill(n[0], n[1]) + + fill(next_square["x"], next_square["y"]) + return np.unique(board, return_counts=True)[-1][-1] \ No newline at end of file diff --git a/main.py b/main.py index 4ca21971..4edc2577 100644 --- a/main.py +++ b/main.py @@ -12,6 +12,7 @@ import random import typing +import numpy as np # info is called when you create your Battlesnake on play.battlesnake.com @@ -22,10 +23,10 @@ def info() -> typing.Dict: return { "apiversion": "1", - "author": "", # TODO: Your Battlesnake Username - "color": "#888888", # TODO: Choose color - "head": "default", # TODO: Choose head - "tail": "default", # TODO: Choose tail + "author": "G", # TODO: Your Battlesnake Username + "color": "#3333ff", # TODO: Choose color + "head": "ski", # TODO: Choose head + "tail": "mystic-moon", # TODO: Choose tail } @@ -42,51 +43,58 @@ def end(game_state: typing.Dict): # move is called on every turn and returns your next move # Valid moves are "up", "down", "left", or "right" # See https://docs.battlesnake.com/api/example-move for available data -def move(game_state: typing.Dict) -> typing.Dict: +def move(game_state: typing.Dict, log=False) -> typing.Dict: - is_move_safe = {"up": True, "down": True, "left": True, "right": True} + from helper_battlesnake import look_ahead + from helper_battlesnake import a_star_search + from helper_battlesnake import obvious_moves + from helper_battlesnake import snake_compass + from helper_battlesnake import flood_fill - # We've included code to prevent your Battlesnake from moving backwards my_head = game_state["you"]["body"][0] # Coordinates of your head - my_neck = game_state["you"]["body"][1] # Coordinates of your "neck" - - if my_neck["x"] < my_head["x"]: # Neck is left of head, don't move left - is_move_safe["left"] = False - - elif my_neck["x"] > my_head["x"]: # Neck is right of head, don't move right - is_move_safe["right"] = False - - elif my_neck["y"] < my_head["y"]: # Neck is below head, don't move down - is_move_safe["down"] = False - - elif my_neck["y"] > my_head["y"]: # Neck is above head, don't move up - is_move_safe["up"] = False - - # TODO: Step 1 - Prevent your Battlesnake from moving out of bounds - # board_width = game_state['board']['width'] - # board_height = game_state['board']['height'] - - # TODO: Step 2 - Prevent your Battlesnake from colliding with itself - # my_body = game_state['you']['body'] - - # TODO: Step 3 - Prevent your Battlesnake from colliding with other Battlesnakes - # opponents = game_state['board']['snakes'] - - # Are there any safe moves left? - safe_moves = [] - for move, isSafe in is_move_safe.items(): - if isSafe: - safe_moves.append(move) + my_length = game_state["you"]["length"] + safe_moves = obvious_moves(game_state, my_head) + if log: + print("=" * 25) + print(f"Safe moves: {safe_moves}") if len(safe_moves) == 0: print(f"MOVE {game_state['turn']}: No safe moves detected! Moving down") return {"move": "down"} - # Choose a random move from the safe ones - next_move = random.choice(safe_moves) - - # TODO: Step 4 - Move towards food instead of random, to regain health and survive longer - # food = game_state['board']['food'] + # Step 4 - Move towards food instead of random, to regain health and survive longer + food = game_state['board']['food'] + food_dist = [] + food_moves = [] + food_fill = [] + # Big idea: loop through all food and find the shortest path using A* search + for food_loc in food: + best_path, best_dist = a_star_search(game_state, my_head.copy(), food_loc) + if best_path is not None: + food_dist.append(best_dist) + way_to_food = snake_compass(my_head, best_path[-2]) + food_moves.append(way_to_food) + f = flood_fill(game_state, look_ahead(my_head, way_to_food)) + food_fill.append(best_dist * 11 * 11 / f ** 2.25) + + # Kinda annoying, but A* sometimes fails because of time complexity. Condition on whether A* ran or not... + if len(food_dist) > 0 and my_length <= 45: + best_dist_to_food = min(food_fill) + best_way_to_food = food_moves[np.argmin(food_fill)] + next_move = best_way_to_food if best_way_to_food in safe_moves else safe_moves[0] + if log: + print("Ran A*") + print(f"Going {next_move} which is {best_dist_to_food} away from {my_head}") + else: + best_fill = -1 + next_move = safe_moves[0] + for safe_move in safe_moves: + end_square = look_ahead(my_head, safe_move) + max_fill = flood_fill(game_state, next_square=end_square) + if max_fill > best_fill: + next_move = safe_move + best_fill = max_fill + print(f"A* failed doing flood fill: {safe_moves}") print(f"MOVE {game_state['turn']}: {next_move}") return {"move": next_move} diff --git a/test.py b/test.py new file mode 100644 index 00000000..054de3b7 --- /dev/null +++ b/test.py @@ -0,0 +1,97 @@ +from helper_battlesnake import * + + +game = { + "game": { + "id": "totally-unique-game-id", + "ruleset": { + "name": "standard", + "version": "v1.1.15", + "settings": { + "foodSpawnChance": 15, + "minimumFood": 1, + "hazardDamagePerTurn": 14 + } + }, + "map": "standard", + "source": "league", + "timeout": 500 + }, + "turn": 14, + "board": { + "height": 11, + "width": 11, + "food": [ + {"x": 5, "y": 5}, + {"x": 9, "y": 0}, + {"x": 2, "y": 6} + ], + "hazards": [ + {"x": 3, "y": 2} + ], + "snakes": [ + { + "id": "snake-508e96ac-94ad-11ea-bb37", + "name": "My Snake", + "health": 54, + "body": [ + {"x": 0, "y": 0}, + {"x": 1, "y": 0}, + {"x": 2, "y": 0} + ], + "latency": "111", + "head": {"x": 0, "y": 0}, + "length": 3, + "shout": "why are we shouting??", + "customizations":{ + "color":"#FF0000", + "head":"pixel", + "tail":"pixel" + } + }, + { + "id": "snake-b67f4906-94ae-11ea-bb37", + "name": "Another Snake", + "health": 16, + "body": [ + {"x": 5, "y": 4}, + {"x": 5, "y": 3}, + {"x": 6, "y": 3}, + {"x": 6, "y": 2} + ], + "latency": "222", + "head": {"x": 5, "y": 4}, + "length": 4, + "shout": "I'm not really sure...", + "customizations":{ + "color":"#26CF04", + "head":"silly", + "tail":"curled" + } + } + ] + }, + "you": { + "id": "snake-508e96ac-94ad-11ea-bb37", + "name": "My Snake", + "health": 54, + "body": [ + {"x": 1, "y": 5}, + {"x": 1, "y": 5}, + {"x": 1, "y": 5} + ], + "latency": "111", + "head": {"x": 1, "y": 5}, + "length": 3, + "shout": "why are we shouting??", + "customizations": { + "color":"#FF0000", + "head":"pixel", + "tail":"pixel" + } + } +} + +a, b = a_star_search(game_state=game, head_loc={"x": 1, "y": 5}, food_loc={"x": 7, "y": 4}) +print(a, b) +print(snake_compass({"x": 1, "y": 5}, a[0])) \ No newline at end of file diff --git a/test_flood_fill.py b/test_flood_fill.py new file mode 100644 index 00000000..3e7d3a03 --- /dev/null +++ b/test_flood_fill.py @@ -0,0 +1,135 @@ +game = { + "game": { + "id": "totally-unique-game-id", + "ruleset": { + "name": "standard", + "version": "v1.1.15", + "settings": { + "foodSpawnChance": 15, + "minimumFood": 1, + "hazardDamagePerTurn": 14 + } + }, + "map": "standard", + "source": "league", + "timeout": 500 + }, + "turn": 14, + "board": { + "height": 11, + "width": 11, + "food": [ + {"x": 2, "y": 0}, + {"x": 1, "y": 9}, + {"x": 4, "y": 6} + ], + "hazards": [ + {"x": 3, "y": 2} + ], + "snakes": [ + { + "id": "snake-508e96ac-94ad-11ea-bb37", + "name": "My Snake", + "health": 54, + "body": [ + {"x": 5, "y": 4}, + {"x": 5, "y": 5}, + {"x": 5, "y": 6}, + {"x": 5, "y": 7}, + {"x": 5, "y": 8}, + {"x": 5, "y": 9}, + {"x": 5, "y": 10}, + # {"x": 5, "y": 7}, + # {"x": 5, "y": 8}, + # {"x": 4, "y": 9} + ], + "latency": "111", + "head": {"x": 0, "y": 0}, + "length": 3, + "shout": "why are we shouting??", + "customizations":{ + "color":"#FF0000", + "head":"pixel", + "tail":"pixel" + } + }, + # { + # "id": "snake-b67f4906-94ae-11ea-bb37", + # "name": "Another Snake", + # "health": 16, + # "body": [ + # {"x": 5, "y": 4}, + # {"x": 5, "y": 3}, + # {"x": 6, "y": 3}, + # {"x": 6, "y": 2} + # ], + # "latency": "222", + # "head": {"x": 5, "y": 4}, + # "length": 4, + # "shout": "I'm not really sure...", + # "customizations":{ + # "color":"#26CF04", + # "head":"silly", + # "tail":"curled" + # } + # } + ] + }, + "you": { + "id": "snake-508e96ac-94ad-11ea-bb37", + "name": "My Snake", + "health": 54, + "body": [ + {"x": 1, "y": 0}, + {"x": 1, "y": 1}, + {"x": 1, "y": 2}, + {"x": 1, "y": 3}, + {"x": 1, "y": 4}, + {"x": 2, "y": 4}, + {"x": 3, "y": 4}, + {"x": 4, "y": 4}, + {"x": 4, "y": 3}, + {"x": 4, "y": 2}, + {"x": 4, "y": 1}, + {"x": 5, "y": 1}, + {"x": 5, "y": 0}, + {"x": 6, "y": 0}, + ], + "latency": "111", + "head": {"x": 1, "y": 0}, + "length": 3, + "shout": "why are we shouting??", + "customizations": { + "color":"#FF0000", + "head":"pixel", + "tail":"pixel" + } + } +} + + +from helper_battlesnake import * + +my_head = {"x": 1, "y": 0} +safe_moves = obvious_moves(game, my_head) +for safe in safe_moves: + f = flood_fill(game, look_ahead(my_head, safe)) + print(safe, f) +print("=" * 50) + +food = game['board']['food'] +food_dist = [] +food_moves = [] +# Big idea: loop through all food and find the shortest path using A* search +for food_loc in food: + best_path, best_dist = a_star_search(game, my_head.copy(), food_loc) + print(f"Food: {food_loc}") + print(f"Shortest dist: {best_dist}") + if best_path is not None: + food_dist.append(best_dist) + lesgo = snake_compass(my_head, best_path[-2]) + food_moves.append(lesgo) + + f = flood_fill(game, look_ahead(my_head, lesgo)) + print(f"Direction {lesgo} at {f}") + print(best_dist * 11 * 11 / f ** 2.25) \ No newline at end of file From d993a087fea7a0933589356b9d52e3233a07b5e9 Mon Sep 17 00:00:00 2001 From: Giabao Tonthat <45995299+Diluent67@users.noreply.github.com> Date: Thu, 5 Oct 2023 12:00:41 +0100 Subject: [PATCH 03/64] Remove the Azure App Service build and deployment workflow config --- .github/workflows/main_battlesnake-g.yml | 63 ------------------------ 1 file changed, 63 deletions(-) delete mode 100644 .github/workflows/main_battlesnake-g.yml diff --git a/.github/workflows/main_battlesnake-g.yml b/.github/workflows/main_battlesnake-g.yml deleted file mode 100644 index d191852e..00000000 --- a/.github/workflows/main_battlesnake-g.yml +++ /dev/null @@ -1,63 +0,0 @@ -# Docs for the Azure Web Apps Deploy action: https://github.com/Azure/webapps-deploy -# More GitHub Actions for Azure: https://github.com/Azure/actions -# More info on Python, GitHub Actions, and Azure App Service: https://aka.ms/python-webapps-actions - -name: Build and deploy Python app to Azure Web App - battlesnake-g - -on: - push: - branches: - - main - workflow_dispatch: - -jobs: - build: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v2 - - - name: Set up Python version - uses: actions/setup-python@v1 - with: - python-version: '3.10' - - - name: Create and start virtual environment - run: | - python -m venv venv - source venv/bin/activate - - - name: Install dependencies - run: pip install -r requirements.txt - - # Optional: Add step to run tests here (PyTest, Django test suites, etc.) - - - name: Upload artifact for deployment jobs - uses: actions/upload-artifact@v2 - with: - name: python-app - path: | - . - !venv/ - - deploy: - runs-on: ubuntu-latest - needs: build - environment: - name: 'Production' - url: ${{ steps.deploy-to-webapp.outputs.webapp-url }} - - steps: - - name: Download artifact from build job - uses: actions/download-artifact@v2 - with: - name: python-app - path: . - - - name: 'Deploy to Azure Web App' - uses: azure/webapps-deploy@v2 - id: deploy-to-webapp - with: - app-name: 'battlesnake-g' - slot-name: 'Production' - publish-profile: ${{ secrets.AZUREAPPSERVICE_PUBLISHPROFILE_332F8044B4DD4F86877E31EF610C4744 }} From 525fb7d13a996d8f43b54c08b209f86020996f22 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Sat, 7 Oct 2023 17:10:12 +0100 Subject: [PATCH 04/64] Transferred over progress --- battlesnake_test.py | 9 ++ helper_battlesnake.py | 108 ++++++++++---- helper_functions.py | 79 ++++++++++ main.py | 173 +++++++++++++++------- main_fr.py | 85 +++++++++++ minimax.py | 331 ++++++++++++++++++++++++++++++++++++++++++ 6 files changed, 706 insertions(+), 79 deletions(-) create mode 100644 battlesnake_test.py create mode 100644 helper_functions.py create mode 100644 main_fr.py create mode 100644 minimax.py diff --git a/battlesnake_test.py b/battlesnake_test.py new file mode 100644 index 00000000..11ea91ae --- /dev/null +++ b/battlesnake_test.py @@ -0,0 +1,9 @@ +from minimax import * + + +game_state = {'game': {'id': 'b4095e43-09b4-47b8-bac5-423bf1737dea', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 156, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_ffjXg8HCP4KxXfJBKCxwYVTF', 'name': 'Nightwing', 'latency': '20', 'health': 99, 'body': [{'x': 9, 'y': 5}, {'+x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 9, 'y': 8}, {'x': 8, 'y': 8}, {'x': 7, 'y': 8}, {'x': 6, 'y': 8}, {'x': 6, 'y': 7}, {'x': 6, 'y': 6}, {'x': 6, 'y': 5}, {'x': 5, 'y': 5}, {'x': 5, 'y': 6}, {'x': 5, 'y': 7}, {'x': 5, 'y': 8}, {'x': 5, 'y': 9}, {'x': 5, 'y': 10}], 'head': {'x': 9, 'y': 5}, 'length': 20, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_CMvfTppGY9jfWTSr4Xq6PWwV', 'name': 'InGodWeRust', 'latency': '24', 'health': 93, 'body': [{'x': 8, 'y': 2}, {'x': 8, 'y': 3}, {'x': 9, 'y': 3}, {'x': 9, 'y': 2}, {'x': 10, 'y': 2}, {'x': 10, 'y': 1}, {'x': 10, 'y': 0}, {'x': 9, 'y': 0}, {'x': 8, 'y': 0}, {'x': 7, 'y': 0}, {'x': 7, 'y': 1}, {'x': 7, 'y': 2}, {'x': 7, 'y': 3}, {'x': 7, 'y': 4}, {'x': 6, 'y': 4}, {'x': 6, 'y': 3}, {'x': 5, 'y': 3}], 'head': {'x': 8, 'y': 2}, 'length': 17, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}], 'food': [{'x': 1, 'y': 7}], 'hazards': []}, 'you': {'id': 'gs_ffjXg8HCP4KxXfJBKCxwYVTF', 'name': 'Nightwing', 'latency': '20', 'health': 99, 'body': [{'x': 9, 'y': 5}, {'x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 9, 'y': 8}, {'x': 8, 'y': 8}, {'x': 7, 'y': 8}, {'x': 6, 'y': 8}, {'x': 6, 'y': 7}, {'x': 6, 'y': 6}, {'x': 6, 'y': 5}, {'x': 5, 'y': 5}, {'x': 5, 'y': 6}, {'x': 5, 'y': 7}, {'x': 5, 'y': 8}, {'x': 5, 'y': 9}, {'x': 5, 'y': 10}], 'head': {'x': 9, 'y': 5}, 'length': 20, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +game_state = {'game': {'id': '7c824fdd-cba6-40a2-b48f-a3c28de68d55', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 2, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_pqhxF7VjWMkrcxfJfTdbRRGP', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 8, 'y': 10}, {'x': 8, 'y': 9}, {'x': 9, 'y': 9}, {'x': 9, 'y': 9}], 'head': {'x': 8, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_Wmqy7TyyVbBbrSj39vCmkWdC', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 2, 'y': 10}, {'x': 2, 'y': 9}, {'x': 1, 'y': 9}, {'x': 1, 'y': 9}], 'head': {'x': 2, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_dfmjdmQfGG7kcYSY9GJBKk8H', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 8, 'y': 0}, {'x': 8, 'y': 1}, {'x': 9, 'y': 1}, {'x': 9, 'y': 1}], 'head': {'x': 8, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_MtXTyh34DfwdCgphMxhMbqy8', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 0, 'y': 2}, {'x': 0, 'y': 1}, {'x': 1, 'y': 1}, {'x': 1, 'y': 1}], 'head': {'x': 0, 'y': 2}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_ffHgjxxHQjHV9Mbt67FYhCvb', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 9, 'y': 5}, {'x': 9, 'y': 5}], 'head': {'x': 10, 'y': 4}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_jP7yypx9XkYGFHrjR96pXRy4', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 4, 'y': 0}, {'x': 4, 'y': 1}, {'x': 5, 'y': 1}, {'x': 5, 'y': 1}], 'head': {'x': 4, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_hgwtX9HJ8r4pfSQS3kbf6P6P', 'name': 'Nightwing', 'latency': '354', 'health': 100, 'body': [{'x': 4, 'y': 10}, {'x': 5, 'y': 10}, {'x': 5, 'y': 9}, {'x': 5, 'y': 9}], 'head': {'x': 4, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_cY7kMdHXjhtWtbD67g8x8CPH', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 0, 'y': 6}, {'x': 0, 'y': 5}, {'x': 1, 'y': 5}, {'x': 1, 'y': 5}], 'head': {'x': 0, 'y': 6}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}], 'food': [{'x': 5, 'y': 5}], 'hazards': []}, 'you': {'id': 'gs_hgwtX9HJ8r4pfSQS3kbf6P6P', 'name': 'Nightwing', 'latency': '354', 'health': 100, 'body': [{'x': 4, 'y': 10}, {'x': 5, 'y': 10}, {'x': 5, 'y': 9}, {'x': 5, 'y': 9}], 'head': {'x': 4, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +game = Battlesnake(game_state) +game.display_board() +next_move = game.minimax_move() +print(next_move) \ No newline at end of file diff --git a/helper_battlesnake.py b/helper_battlesnake.py index 579159d5..22b80a8f 100644 --- a/helper_battlesnake.py +++ b/helper_battlesnake.py @@ -2,18 +2,16 @@ import typing -def look_ahead(head_loc, direction): - """ - Given the snake's coordinates and a possible move, return the snake's new coordinates if it were headed that way - """ - snake_head = head_loc.copy() - if direction == "up": +def look_ahead(head, move): + """Given a possible move, return the snake's new coordinates if it were headed that way""" + snake_head = head.copy() + if move == "up": snake_head["y"] += 1 - if direction == "down": + if move == "down": snake_head["y"] -= 1 - if direction == "left": + if move == "left": snake_head["x"] -= 1 - if direction == "right": + if move == "right": snake_head["x"] += 1 return snake_head @@ -57,6 +55,7 @@ def obvious_moves(game_state: typing.Dict, my_head: typing.Dict, my_neck=None, r """ is_move_safe = {"up": True, "down": True, "left": True, "right": True} + my_length = game_state["you"]["length"] if my_neck is None: my_neck = game_state["you"]["body"][1] # Coordinates of your "neck" # print(f"Head location: {str(my_head)}") @@ -74,7 +73,7 @@ def obvious_moves(game_state: typing.Dict, my_head: typing.Dict, my_neck=None, r is_move_safe["up"] = False # print("Neck is above head, don't move up") - # Step 1 - Prevent your Battlesnake from moving out of bounds + # Prevent your Battlesnake from moving out of bounds board_width = game_state['board']['width'] board_height = game_state['board']['height'] @@ -91,7 +90,7 @@ def obvious_moves(game_state: typing.Dict, my_head: typing.Dict, my_neck=None, r is_move_safe["up"] = False # print("Out of bounds, can't move up") - # Step 2 - Prevent your Battlesnake from colliding with itself + # Prevent your Battlesnake from colliding with itself my_body = game_state['you']['body'] for direction in refresh_safe_moves(is_move_safe): possible_move = look_ahead(my_head, direction) @@ -101,7 +100,7 @@ def obvious_moves(game_state: typing.Dict, my_head: typing.Dict, my_neck=None, r # print(f"Headed towards: {str(possible_move)}") # print(f"Body: {str(my_body)}") - # Step 3 - Prevent your Battlesnake from colliding with other Battlesnakes + # Prevent your Battlesnake from colliding with other Battlesnakes opponents = game_state['board']['snakes'] for opponent in opponents: if opponent["id"] == game_state['you']["id"]: # Ignore yourself @@ -112,9 +111,9 @@ def obvious_moves(game_state: typing.Dict, my_head: typing.Dict, my_neck=None, r for direction in ["up", "down", "left", "right"]: possible_move = look_ahead(my_head, direction) # Avoid any possible encounters with any future opponent move - if risk_averse: - opponent_move = [look_ahead(opponent_head, direction) for direction in ["up", "down", "left", "right"]] - if possible_move in opponent_move: + if risk_averse and my_length < opponent["length"]: + opponent_moves = [look_ahead(opponent_head, direction) for direction in ["up", "down", "left", "right"]] + if possible_move in opponent_moves: is_move_safe[direction] = False # Avoid running into the opponent snake if possible_move in opponent_locs: @@ -139,17 +138,17 @@ def __repr__(self): return str(self.location) -def a_star_search(game_state, head_loc, food_loc): +def a_star_search(game_state, my_head, food_loc): """ Implement the A* search algorithm to find the shortest path to food """ + head_loc = my_head.copy() open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet closed_list = [] # Contains the nodes which are examined timer = 0 while len(open_list) > 0: - if timer > 200: - print("Past 200") + if timer > 300: return None, None current_node = min(open_list, key = lambda node: node.f) # The node with the smallest f value @@ -169,7 +168,7 @@ def a_star_search(game_state, head_loc, food_loc): game_state, my_head=current_node.location, my_neck=current_node.parent.location if current_node.parent is not None else None, - risk_averse=False + risk_averse=True ) for possible_move in possible_moves: @@ -198,22 +197,34 @@ def a_star_search(game_state, head_loc, food_loc): return None, None -def flood_fill(game_state, next_square): +def flood_fill(game_state, next_square, risk_averse=True): + """ + Recursive function to get the total area of the current fill selection + """ board_width = game_state['board']['width'] board_height = game_state['board']['height'] - board = np.zeros((board_width, board_height)) + my_length = game_state["you"]["length"] + # Make an (l x w) array and simulate the board - for pixel in game_state["you"]["body"]: - board[pixel["x"], pixel["y"]] = 1 + board = np.zeros((board_width, board_height)) for snake in game_state["board"]["snakes"]: body_pixels = snake["body"] for pixel in body_pixels: board[pixel["x"], pixel["y"]] = 1 + if snake["id"] == game_state['you']["id"]: # Ignore yourself + continue + elif risk_averse and my_length < snake["length"]: + opponent_head = body_pixels[0] + opponent_moves = [look_ahead(opponent_head, direction) for direction in ["up", "down", "left", "right"]] + for m in opponent_moves: + if 0 <= m["x"] < board_width and 0 <= m["y"] < board_height: + board[m["x"], m["y"]] = 1 + def fill(x, y): - if board[x][y] == 1: + if board[x][y] == 1: # Snakes or hazards return - if board[x][y] == 2: + if board[x][y] == 2: # Fill return board[x][y] = 2 neighbours = [(x-1,y),(x+1,y),(x,y-1),(x,y+1)] @@ -222,4 +233,49 @@ def fill(x, y): fill(n[0], n[1]) fill(next_square["x"], next_square["y"]) - return np.unique(board, return_counts=True)[-1][-1] \ No newline at end of file + return sum((row == 2).sum() for row in board) + + +def fill_search(game_state, my_head, safe_moves, log): + best_fill = -1 + next_move = safe_moves[0] + for safe_move in safe_moves: + end_square = look_ahead(my_head, safe_move) + max_fill = flood_fill(game_state, next_square=end_square) + if max_fill > best_fill: + next_move = safe_move + best_fill = max_fill + if log: + print(f"Flood fill on {safe_move} and got {max_fill}") + return next_move + +def order_food(game_state, head_loc): + """ + Return a sorted list of food options by distance to the snake. Also decide a cut-off based on health + """ + menu = game_state["board"]["food"] + health = game_state["you"]["health"] + # For each food item, grab the Manhattan distance to the snake + food_costs = [] + for food in menu: + cost_to_item = manhattan_distance(head_loc, food) + food_costs.append(cost_to_item) + + def helper_food_filter(menu, food_costs, cutoff): + menu_indices = np.where(np.array(food_costs) <= cutoff) + filt_menu = menu[menu_indices] + return + + # Sort the food by the distances and apply discretionary filters based on how hungry you are + sorted_menu = sorted(menu, key=lambda x: food_costs[menu.index(x)]) + sorted_food_costs = sorted(food_costs) + if len(menu) == 0: + new_menu = [] + elif health >= 45: # Big chilling + new_menu = [x for x in sorted_menu if sorted_food_costs[sorted_menu.index(x)] <= 5] + elif health <= min(food_costs) * 3: # Pretty desperate + new_menu = [x for x in sorted_menu if sorted_food_costs[sorted_menu.index(x)] <= 15] + else: + new_menu = [x for x in sorted_menu if sorted_food_costs[sorted_menu.index(x)] <= 10] + + return new_menu diff --git a/helper_functions.py b/helper_functions.py new file mode 100644 index 00000000..644d635f --- /dev/null +++ b/helper_functions.py @@ -0,0 +1,79 @@ +import copy +import time +import numpy as np +import typing + + +def look_ahead(head, move): + """Given a possible move, return the snake's new coordinates if it were headed that way""" + snake_head = head.copy() + if move == "up": + snake_head["y"] += 1 + if move == "down": + snake_head["y"] -= 1 + if move == "left": + snake_head["x"] -= 1 + if move == "right": + snake_head["x"] += 1 + return snake_head + +def simulate_move(game_state, move, snake_id): + """Given a game_state variable, simulate a board with a given move for a given snake""" + game_state2 = game_state + all_snakes = game_state2["board"]["snakes"] + head = [snake for snake in all_snakes if snake["id"] == snake_id][0]["head"] + new_head = look_ahead(head, move) + + snake_index = [snake["id"] for snake in all_snakes].index(snake_id) + game_state2["board"]["snakes"][snake_index]["body"] = [new_head] + game_state2["board"]["snakes"][snake_index]["body"][:-1] # Remove tail + if snake_id == game_state2["you"]["id"]: + game_state2["you"]["body"] = [new_head] + game_state2["you"]["body"][:-1] # Remove tail + game_state2["you"]["head"] = new_head + return game_state2 + +def calc_manhattan_distance(start, end): + """Return the Manhattan distance for two positions""" + start = (start["x"], start["y"]) + end = (end["x"], end["y"]) + return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) + +def flood_fill(game_state, risk_averse=True): + """Recursive function to get the total area of the current fill selection""" + clock_in = time.time_ns() + board_width = game_state["board"]["width"] + board_height = game_state["board"]["height"] + my_head = game_state["you"]["head"] + my_length = game_state["you"]["length"] + + # Make an (l x w) array and simulate the board + board = np.empty((board_width, board_height), dtype="______>___|__(______/__|__\\_____> # -# This file can be a nice home for your Battlesnake logic and helper functions. -# -# To get you started we've included code to prevent your Battlesnake from moving backwards. -# For more info see docs.battlesnake.com +import numpy as np import random +import time import typing -import numpy as np +from helper_battlesnake import a_star_search +from helper_battlesnake import fill_search +from helper_battlesnake import flood_fill +from helper_battlesnake import look_ahead +from helper_battlesnake import obvious_moves +from helper_battlesnake import order_food +from helper_battlesnake import snake_compass + + +global board_log -# info is called when you create your Battlesnake on play.battlesnake.com -# and controls your Battlesnake's appearance -# TIP: If you open your Battlesnake URL in a browser you should see this data def info() -> typing.Dict: + """ + info is called when you create your Battlesnake on play.battlesnake.com + and controls your Battlesnake's appearance + TIP: If you open your Battlesnake URL in a browser you should see this data + """ print("INFO") return { "apiversion": "1", - "author": "G", # TODO: Your Battlesnake Username - "color": "#3333ff", # TODO: Choose color - "head": "ski", # TODO: Choose head - "tail": "mystic-moon", # TODO: Choose tail + "author": "G", + "color": "#3333ff", + "head": "ski", + "tail": "mystic-moon", } -# start is called when your Battlesnake begins a game def start(game_state: typing.Dict): + """start is called when your Battlesnake begins a game""" print("GAME START") + global board_log + board_log = open("board_log.txt", "w") -# end is called when your Battlesnake finishes a game def end(game_state: typing.Dict): + """end is called when your Battlesnake finishes a game""" print("GAME OVER\n") -# move is called on every turn and returns your next move -# Valid moves are "up", "down", "left", or "right" -# See https://docs.battlesnake.com/api/example-move for available data -def move(game_state: typing.Dict, log=False) -> typing.Dict: - - from helper_battlesnake import look_ahead - from helper_battlesnake import a_star_search - from helper_battlesnake import obvious_moves - from helper_battlesnake import snake_compass - from helper_battlesnake import flood_fill - +def move(game_state: typing.Dict, log=True, timer_log=True) -> typing.Dict: + """ + move is called on every turn and returns your next move + Valid moves are "up", "down", "left", or "right" + See https://docs.battlesnake.com/api/example-move for available data + """ + clock_in = time.time_ns() my_head = game_state["you"]["body"][0] # Coordinates of your head - my_length = game_state["you"]["length"] + my_length = game_state["you"]["length"] # Length of your snake + + # Find the safe moves from current position safe_moves = obvious_moves(game_state, my_head) + if timer_log: + print(f"Found safe moves in {(time.time_ns() - clock_in) // 1000000} ms") if log: - print("=" * 25) print(f"Safe moves: {safe_moves}") if len(safe_moves) == 0: - print(f"MOVE {game_state['turn']}: No safe moves detected! Moving down") - return {"move": "down"} - - # Step 4 - Move towards food instead of random, to regain health and survive longer - food = game_state['board']['food'] + risky_moves = obvious_moves(game_state, my_head, risk_averse=False) + if len(risky_moves) == 1: + return {"move": risky_moves[0]} + elif len(risky_moves) == 0: + print(f"MOVE {game_state['turn']}: No safe moves detected! Moving down") + return {"move": "down"} + else: + safe_moves = risky_moves + + # Move towards food instead of random, to regain health and survive longer food_dist = [] food_moves = [] food_fill = [] - # Big idea: loop through all food and find the shortest path using A* search - for food_loc in food: - best_path, best_dist = a_star_search(game_state, my_head.copy(), food_loc) + food_heur = [] + # Big idea: loop through the most convenient food and find the shortest path using A* search + sorted_food_list = order_food(game_state, my_head) + if log: + print(f"Narrowed down {len(sorted_food_list)} food options: {sorted_food_list}") + for food_loc in sorted_food_list: + clock_in = time.time_ns() + best_path, best_dist = a_star_search(game_state, my_head, food_loc) + if timer_log: + if best_path is None: + print(f"Failed A* search in {(time.time_ns() - clock_in) // 1000000} ms") + else: + print(f"Ran A* search successfully in {(time.time_ns() - clock_in) // 1000000} ms") + + # Grab the output if A* search ran to completion AND run flood fill just in case if best_path is not None: + clock_in = time.time_ns() + # Grab the shortest distance to the food food_dist.append(best_dist) way_to_food = snake_compass(my_head, best_path[-2]) + # Grab the direction to the food food_moves.append(way_to_food) f = flood_fill(game_state, look_ahead(my_head, way_to_food)) - food_fill.append(best_dist * 11 * 11 / f ** 2.25) + food_fill.append(f) + # Grab the heuristic computed for the food + heuristic = best_dist * 11 * 11 / f ** 2.25 + food_heur.append(heuristic) + + if timer_log: + print(f"Ran flood fill and computed heuristic in {(time.time_ns() - clock_in) // 1000000} ms") + if log: + print(f"Distance: {best_dist}, Fill: {f}, Heuristic: {round(heuristic, 5)}") # Kinda annoying, but A* sometimes fails because of time complexity. Condition on whether A* ran or not... - if len(food_dist) > 0 and my_length <= 45: - best_dist_to_food = min(food_fill) - best_way_to_food = food_moves[np.argmin(food_fill)] - next_move = best_way_to_food if best_way_to_food in safe_moves else safe_moves[0] - if log: - print("Ran A*") - print(f"Going {next_move} which is {best_dist_to_food} away from {my_head}") - else: - best_fill = -1 - next_move = safe_moves[0] - for safe_move in safe_moves: - end_square = look_ahead(my_head, safe_move) - max_fill = flood_fill(game_state, next_square=end_square) - if max_fill > best_fill: - next_move = safe_move - best_fill = max_fill - print(f"A* failed doing flood fill: {safe_moves}") + food_risk_too_high = False + if len(food_dist) > 0 and my_length <= 25: # If there's food and we're not chilling + next_fill = min(food_fill) + # Probably going to get trapped if we go here + if next_fill < 25: + food_risk_too_high = True + if log: + print(f"If we eat, the fill is {next_fill} so skip") + + if not food_risk_too_high: + next_move = food_moves[np.argmin(food_heur)] + # This bug shouldn't happen + if next_move not in safe_moves: + raise ValueError + if log: + best_food_loc = sorted_food_list[np.argmin(food_heur)] + print(f"Best A* search is {best_food_loc}") + print(f"Going {next_move} which is {min(food_dist)} away from {my_head}") + print(f"MOVE {game_state['turn']}: {next_move}") + print("=" * 10) + return {"move": next_move} + + best_fill = -1 + next_move = safe_moves[0] + for safe_move in safe_moves: + end_square = look_ahead(my_head, safe_move) + max_fill = flood_fill(game_state, next_square=end_square) + if max_fill > best_fill: + next_move = safe_move + best_fill = max_fill + + + # Optimise based on flood fill + next_move = fill_search(game_state, my_head, safe_moves, log) + + if best_fill < 5: + risky_moves = obvious_moves(game_state, my_head, risk_averse=False) + print(f"Risk-averse may cause death with fill = {best_fill}") + next_move = fill_search(game_state, my_head, risky_moves, log) + print(f"Changing to risky with fill = {best_fill}") + if log: + print(f"No A * but best flood fill: {next_move}") print(f"MOVE {game_state['turn']}: {next_move}") + print("=" * 10) + # board_log.write(f"MOVE {game_state['turn']}\n") + # board_log.write(str(game_state)) + # board_log.write("\n" + "=" * 100) return {"move": next_move} diff --git a/main_fr.py b/main_fr.py new file mode 100644 index 00000000..9ac285e0 --- /dev/null +++ b/main_fr.py @@ -0,0 +1,85 @@ +# Welcome to +# __________ __ __ .__ __ +# \______ \_____ _/ |__/ |_| | ____ ______ ____ _____ | | __ ____ +# | | _/\__ \\ __\ __\ | _/ __ \ / ___// \\__ \ | |/ // __ \ +# | | \ / __ \| | | | | |_\ ___/ \___ \| | \/ __ \| <\ ___/ +# |________/(______/__| |__| |____/\_____>______>___|__(______/__|__\\_____> +# + +import numpy as np +import random +import time +import typing + +from minimax import Battlesnake + + +global board_log + +def info() -> typing.Dict: + """ + info is called when you create your Battlesnake on play.battlesnake.com + and controls your Battlesnake's appearance + TIP: If you open your Battlesnake URL in a browser you should see this data + """ + print("INFO") + + return { + "apiversion": "1", + "author": "G", + "color": "#3333ff", + "head": "ski", + "tail": "mystic-moon", + } + + +def start(game_state: typing.Dict): + """start is called when your Battlesnake begins a game""" + print("GAME START") + global board_log + board_log = open("board_log.txt", "w") + + +def end(game_state: typing.Dict): + """end is called when your Battlesnake finishes a game""" + print("GAME OVER\n") + + +def move(game_state: typing.Dict, log=True, timer_log=True) -> typing.Dict: + """ + move is called on every turn and returns your next move + Valid moves are "up", "down", "left", or "right" + See https://docs.battlesnake.com/api/example-move for available data + """ + + game = Battlesnake(game_state) + + # Find the safe moves from current position + clock_in = time.time_ns() + optimal_move = game.minimax_move() + if timer_log: + print(f"Found optimal move with Minimax in {(time.time_ns() - clock_in) // 1000000} ms") + if log: + print(f"Safe moves: {optimal_move}") + + if optimal_move is None: + risky_move = game.minimax_move(risk_averse=False) + if risky_move is not None: + optimal_move = risky_move + else: + print(f"MOVE {game_state['turn']}: No safe moves detected! Moving down") + return {"move": "down"} + + print(f"MOVE {game_state['turn']}: {optimal_move}") + print("=" * 10) + board_log.write(f"MOVE {game_state['turn']}\n") + board_log.write(str(game_state)) + board_log.write("\n" + "=" * 100) + return {"move": optimal_move} + + +# Start server when `python main.py` is run +if __name__ == "__main__": + from server import run_server + + run_server({"info": info, "start": start, "move": move, "end": end}) diff --git a/minimax.py b/minimax.py new file mode 100644 index 00000000..35a576f3 --- /dev/null +++ b/minimax.py @@ -0,0 +1,331 @@ +import itertools +import numpy as np +import time +from helper_functions import look_ahead, simulate_move, calc_manhattan_distance, flood_fill + +global counter +counter = 0 + + +class Battlesnake: + def __init__(self, game_state): + self.game_state = game_state + self.board_width = game_state["board"]["width"] + self.board_height = game_state["board"]["height"] + self.all_snakes = game_state["board"]["snakes"] + self.food = game_state["board"]["food"] + self.hazards = game_state["board"]["hazards"] + self.board = np.empty((self.board_width, self.board_height), dtype=" head["x"]: # Neck is right of head, don't move right + possible_moves.discard("right") + elif neck["y"] < head["y"]: # Neck is below head, don't move down + possible_moves.discard("down") + elif neck["y"] > head["y"]: # Neck is above head, don't move up + possible_moves.discard("up") + + # Prevent snake from moving out of bounds + if head["x"] == 0: + possible_moves.discard("left") + if head["x"] + 1 == self.board_height: + possible_moves.discard("right") + if head["y"] == 0: + possible_moves.discard("down") + if head["y"] + 1 == self.board_width: + possible_moves.discard("up") + + # Prevent snake from colliding with other snakes + if not allow_suicide: # Unless the snake is willing to die + for opp_snake in self.all_snakes: + for move in possible_moves.copy(): # Remaining moves + # If the potential move hits a snake's body, it's invalid (but not the tail since it moves forward) + if look_ahead(head, move) in opp_snake["body"][:-1]: + possible_moves.discard(move) + # Avoid possible collisions with any future moves an opponent snake can make + if risk_averse: + snake_length = [snake for snake in self.all_snakes if snake["id"] == snake_id][0]["length"] + # Only if the snake in question is shorter + if opp_snake["id"] != snake_id and snake_length < opp_snake["length"]: + opp_moves = [look_ahead(opp_snake["head"], move2) for move2 in ["up", "down", "left", "right"]] + if look_ahead(head, move) in opp_moves: + possible_moves.discard(move) + + print(f"Identified obvious moves {list(possible_moves)} in {(time.time_ns() - clock_in) // 1000000} ms") + return list(possible_moves) + + def minimax_move(self, risk_averse=True): + # Compute the best score of each move using the minimax algorithm with alpha-beta pruning + minimax_score, best_move = self.minimax(self.game_state, depth=3, alpha=-np.inf, beta=np.inf, maximising_snake=True) + return best_move + + def is_game_over(self, game_state=None, maximising_snake=True): + """Return True if our snake won or lost, False otherwise""" + # Just if you want to simulate moves for a different game state + input_game = game_state if game_state is not None else self.game_state + all_snakes = input_game["board"]["snakes"] + if maximising_snake: # This means it's our turn + snake_heads = [input_game["you"]["head"]] + snake_lengths = [input_game["you"]["length"]] + snake_ids = [self.my_id] + else: # Opponent's turn + snake_heads = [snake["head"] for snake in all_snakes if snake["id"] != self.my_id] + snake_lengths = [snake["length"] for snake in all_snakes if snake["id"] != self.my_id] + snake_ids = [snake["id"] for snake in all_snakes if snake["id"] != self.my_id] + + snakes_alive = [True] * len(snake_heads) # Initialise a boolean list for each snake + for i in range(len(snake_heads)): + coords = snake_heads[i] + # Prevent snake from moving out of bounds + if coords["x"] < 0 or coords["x"] >= self.board_height: + snakes_alive[i] = False + if coords["y"] < 0 or coords["y"] >= self.board_width: + snakes_alive[i] = False + + # Prevent snake from colliding with other snakes + for opp_snake in all_snakes: + if opp_snake["id"] != snake_ids[i]: + # If the potential move hits a snake's body, it's invalid + if coords in opp_snake["body"][1:]: + snakes_alive[i] = False + # If the potential move hits a snake's head, it's invalid only if it's shorter + if coords == opp_snake["body"][0] and snake_lengths[i] <= opp_snake["length"]: + snakes_alive[i] = False + + return True if sum(snakes_alive) == 0 else False + + def heuristic(self, game_state): + return flood_fill(game_state, risk_averse=True) + + def store_board_positions(self, game_state): + """Things get confusing when we're simulating snake positions over and over. Making shallow copies of game_state + variables to simulate a new position causes a loss of information in the original board, and making deep copies + adds too much to the runtime. + + Solution: this function can keep a temporary copy of all the positions of all snakes and can be re-inserted into + the game_state variable at any point to "restore" the original board.""" + # Grab our position + copy_our_pos = game_state["you"]["body"].copy() + + # Grab opponents' positions + # We need the index of each opponent snake in the game_state's "snakes" field for later use + num_snakes = len(game_state["board"]["snakes"]) + copy_opps_bodies = [game_state["board"]["snakes"][index]["body"] for index in range(num_snakes)] + copy_opps_pos = {index: copy_opps_bodies[index] for index in range(num_snakes)} + return {"you": copy_our_pos, "snakes": copy_opps_pos} + + @staticmethod + def restore_board_positions(game_state, snake_position_dict): + """Goes hand-in-hand with the store_board_position function""" + # Restore our positions + copy_our_pos = snake_position_dict["you"] + game_state["you"]["body"] = copy_our_pos + game_state["you"]["head"] = copy_our_pos[0] + game_state["you"]["length"] = len(copy_our_pos) + + # Replace all other snakes' positions + copy_opps_pos = snake_position_dict["snakes"] + for index, body in copy_opps_pos.items(): + game_state["board"]["snakes"][index]["body"] = copy_opps_pos[index] + game_state["board"]["snakes"][index]["head"] = copy_opps_pos[index][0] + game_state["board"]["snakes"][index]["length"] = len(copy_opps_pos[index]) + return game_state + + def minimax(self, game_state, depth, alpha, beta, maximising_snake): + """Implement the minimax algorithm with alpha-beta pruning + + :param game_state: A fresh copy of the game state dictionary (not from self.game_state in case we need to + simulate different situations) + :param depth: + :param alpha: + :param beta: + :param maximising_snake: + :return: + """ + global counter + counter += 1 + + # At the bottom of the decision tree or if we won/lost the game + if self.is_game_over(game_state, maximising_snake): + print("=" * 50) + print(f"Heuristic = -100 at terminal node of depth = {depth}") + return -100, None + if depth == 0: + heur = self.heuristic(game_state) + print("=" * 50) + print(f"Heuristic = {heur} at terminal node of depth = {depth}") + return heur, None + + # Our snake's turn + if maximising_snake: + clock_in = time.time_ns() + print("=" * 50) + print(f"DEPTH == {depth} OUR SNAKE") + possible_moves = self.get_obvious_moves(game_state, risk_averse=False) + best_val, best_move = -np.inf, None + + # Store the positions of all snakes before making any board simulations + stored_snake_positions = self.store_board_positions(game_state) + + for num, move in enumerate(possible_moves): + # Restore game state after the first simulation + if num > 0: + game_state = self.restore_board_positions(game_state, stored_snake_positions) + + sim_game_state = simulate_move(game_state, move, self.my_id) + clock_in2 = time.time_ns() + print(f"Running Minimax for OUR SNAKE'S move {move} at depth = {depth} at counter = {counter}") + node_val, node_move = self.minimax(sim_game_state, depth - 1, alpha, beta, False) + + # Update best score and best move + if np.argmax([best_val, node_val]) == 1: + best_move = node_move if node_move is not None else move # None when it's a terminal node + best_val = max(best_val, node_val) + + print("=" * 50) + print(f"BACK AT DEPTH = {depth}") + print(f"Heuristic = {best_val} and best move = {best_move} at our node at depth = {depth - 1} in {(time.time_ns() - clock_in2) // 1000000} ms") + + # Check to see if we can prune + alpha = max(alpha, best_val) + if alpha >= beta: + print("PRUNED!!!") + break + + print(f"Minimax on our snake in {(time.time_ns() - clock_in) // 1000000} ms") + return best_val, best_move + + # Opponent snakes' turns + else: + clock_in = time.time_ns() + print("=" * 50) + print(f"DEPTH == {depth} OPPONENT SNAKES") + + # Grab possible moves for all opponent snakes + all_opps_ids = [] + all_opps_moves = [] # Going to be a list of lists + for opp_snake in self.opponents: + opp_id = opp_snake["id"] + # But only within a reasonable distance of our own snake + if calc_manhattan_distance(self.my_head, opp_snake["head"]) < self.board_width // 2: + all_opps_moves.append(self.get_obvious_moves(game_state, snake_id=opp_id, allow_suicide=True)) + all_opps_ids.append(opp_id) + print(f"{len(all_opps_ids)} OPPONENT SNAKES within {self.board_width // 2} of us in {(time.time_ns() - clock_in) // 1000000} ms") + + # Store the positions of all snakes before making any board simulations + stored_snake_positions = self.store_board_positions(game_state) + + clock_in = time.time_ns() + possible_sim_states = [] + possible_movesets = [] + # Get all possible boards by simulating moves for each opponent snake, one at a time + all_opp_combos = list(itertools.product(*all_opps_moves)) + for num, move_combo in enumerate(all_opp_combos): + # Restore game state after the first simulation + if num > 0: + game_state = self.restore_board_positions(game_state, stored_snake_positions) + + sim_game_state = game_state.copy() + for snake_num, move in enumerate(move_combo): + sim_game_state = simulate_move(sim_game_state, move, all_opps_ids[snake_num]) + possible_sim_states.append(self.store_board_positions(sim_game_state)) + possible_movesets.append(move_combo) + print(f"{len(possible_sim_states)} POSSIBLE SIMULATED BOARDS OF OPPONENT MOVES in {(time.time_ns() - clock_in) // 1000000} ms") + + best_val, best_move = np.inf, None + game_state = self.restore_board_positions(game_state, stored_snake_positions) + for num, stored_sim in enumerate(possible_sim_states): + clock_in2 = time.time_ns() + sim_state = self.restore_board_positions(game_state, stored_sim) + print(f"TESTING {possible_movesets[num]}") + Battlesnake(sim_state).display_board() + print(f"Running Minimax for OPPONENT SNAKES' moves at depth = {depth} at counter = {counter}") + node_val, node_move = self.minimax(sim_state, depth - 1, alpha, beta, True) + + # Update best score and best move + best_val = min(best_val, node_val) + if np.argmin([best_val, node_val]) == 1: + best_move = node_move if node_move is not None else possible_movesets[num] + + print("=" * 50) + print(f"BACK AT DEPTH = {depth}") + print(f"Heuristic = {best_val} and best move = {best_move} at opponents' node at depth = {depth - 1} in {(time.time_ns() - clock_in2) // 1000000} ms") + + # Check to see if we can prune + beta = min(beta, best_val) + if beta <= alpha: + print("PRUNED!!!") + break + + print(f"Minimax on opponents took {(time.time_ns() - clock_in) // 1000000} ms") + return best_val, best_move \ No newline at end of file From 35fec40ff5e79fb21620d0cd8051fba6178b9b06 Mon Sep 17 00:00:00 2001 From: Giabao Tonthat Date: Wed, 11 Oct 2023 09:59:15 +0100 Subject: [PATCH 05/64] New changes --- battlesnake_test.py | 34 +- helper_functions.py | 79 ----- minimax.py | 800 ++++++++++++++++++++++++++++++++------------ requirements.txt | 2 + 4 files changed, 614 insertions(+), 301 deletions(-) delete mode 100644 helper_functions.py diff --git a/battlesnake_test.py b/battlesnake_test.py index 11ea91ae..d6888818 100644 --- a/battlesnake_test.py +++ b/battlesnake_test.py @@ -1,9 +1,37 @@ from minimax import * +# game_state = {'game': {'id': 'c05521ea-91ad-4f2f-90d0-1fc2061965d4', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 5, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_WYfcQxBtYbg9DV3YRRg8dKd9', 'name': 'Nightwing', 'latency': '54', 'health': 95, 'body': [{'x': 8, 'y': 7}, {'x': 9, 'y': 7}, {'x': 10, 'y': 7}], 'head': {'x': 8, 'y': 7}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_gWqyHSwhtDxTtQ8mSymfrQvb', 'name': 'viper', 'latency': '500', 'health': 95, 'body': [{'x': 1, 'y': 10}, {'x': 1, 'y': 9}, {'x': 1, 'y': 8}], 'head': {'x': 1, 'y': 10}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#e04c07', 'head': 'missile', 'tail': 'nr-booster'}}, {'id': 'gs_p3WmqtmFvXtqbmbWcWB4JfH8', 'name': 'ricksnek', 'latency': '114', 'health': 99, 'body': [{'x': 5, 'y': 0}, {'x': 4, 'y': 0}, {'x': 4, 'y': 1}, {'x': 4, 'y': 2}], 'head': {'x': 5, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_y9BxCH4tHt4yGvYpTbvPTMSH', 'name': 'Lispy', 'latency': '11', 'health': 97, 'body': [{'x': 6, 'y': 7}, {'x': 6, 'y': 8}, {'x': 6, 'y': 9}, {'x': 6, 'y': 10}], 'head': {'x': 6, 'y': 7}, 'length': 4, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_SJf3kDmFT3hBmjH8GqgbhjBP', 'name': 'InGodWeRust', 'latency': '28', 'health': 97, 'body': [{'x': 10, 'y': 9}, {'x': 9, 'y': 9}, {'x': 8, 'y': 9}, {'x': 8, 'y': 10}], 'head': {'x': 10, 'y': 9}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_89CJrywcHJfy7pg3BcFdVgvX', 'name': 'colin', 'latency': '500', 'health': 95, 'body': [{'x': 1, 'y': 6}, {'x': 1, 'y': 5}, {'x': 1, 'y': 4}], 'head': {'x': 1, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#6600ff', 'head': 'all-seeing', 'tail': 'weight'}}, {'id': 'gs_KwpcQXb3hxXqRpCrc8SgbHcF', 'name': 'danger-noodle', 'latency': '500', 'health': 95, 'body': [{'x': 9, 'y': 6}, {'x': 9, 'y': 5}, {'x': 9, 'y': 4}], 'head': {'x': 9, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 10, 'y': 4}, {'x': 0, 'y': 6}, {'x': 2, 'y': 0}, {'x': 8, 'y': 0}, {'x': 0, 'y': 8}, {'x': 5, 'y': 5}, {'x': 2, 'y': 3}, {'x': 2, 'y': 1}], 'hazards': []}, 'you': {'id': 'gs_WYfcQxBtYbg9DV3YRRg8dKd9', 'name': 'Nightwing', 'latency': '54', 'health': 95, 'body': [{'x': 8, 'y': 7}, {'x': 9, 'y': 7}, {'x': 10, 'y': 7}], 'head': {'x': 8, 'y': 7}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +# PROBLEM +# game_state = {'game': {'id': '2babaad2-8d7d-42fc-b107-c4848c7c20dc', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 144, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_78cQV3vbT8CfDPSCQytS9xbQ', 'name': 'Nightwing', 'latency': '52', 'health': 76, 'body': [{'x': 9, 'y': 7}, {'x': 9, 'y': 8}, {'x': 9, 'y': 9}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}, {'x': 3, 'y': 9}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 2, 'y': 9}, {'x': 2, 'y': 8}, {'x': 1, 'y': 8}, {'x': 1, 'y': 7}, {'x': 2, 'y': 7}], 'head': {'x': 9, 'y': 7}, 'length': 18, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_MTDX6KSj9TbtTktmy9HkMFVC', 'name': 'InGodWeRust', 'latency': '26', 'health': 96, 'body': [{'x': 9, 'y': 3}, {'x': 9, 'y': 2}, {'x': 9, 'y': 1}, {'x': 9, 'y': 0}, {'x': 8, 'y': 0}, {'x': 8, 'y': 1}, {'x': 8, 'y': 2}, {'x': 7, 'y': 2}, {'x': 7, 'y': 3}, {'x': 6, 'y': 3}, {'x': 6, 'y': 4}, {'x': 6, 'y': 5}, {'x': 6, 'y': 6}, {'x': 7, 'y': 6}, {'x': 7, 'y': 7}, {'x': 7, 'y': 8}, {'x': 7, 'y': 9}], 'head': {'x': 9, 'y': 3}, 'length': 17, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}], 'food': [{'x': 3, 'y': 3}, {'x': 9, 'y': 4}], 'hazards': []}, 'you': {'id': 'gs_78cQV3vbT8CfDPSCQytS9xbQ', 'name': 'Nightwing', 'latency': '52', 'health': 76, 'body': [{'x': 9, 'y': 7}, {'x': 9, 'y': 8}, {'x': 9, 'y': 9}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}, {'x': 3, 'y': 9}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 2, 'y': 9}, {'x': 2, 'y': 8}, {'x': 1, 'y': 8}, {'x': 1, 'y': 7}, {'x': 2, 'y': 7}], 'head': {'x': 9, 'y': 7}, 'length': 18, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +game_state = {'game': {'id': 'fd6dc64e-244e-4744-a2d2-15dbb8adbc29', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 114, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_39MfCDjtdBpQ7fyWMJxTQdqB', 'name': 'Lispy', 'latency': '10', 'health': 98, 'body': [{'x': 10, 'y': 2}, {'x': 10, 'y': 1}, {'x': 10, 'y': 0}, {'x': 9, 'y': 0}, {'x': 8, 'y': 0}, {'x': 7, 'y': 0}, {'x': 6, 'y': 0}, {'x': 5, 'y': 0}, {'x': 5, 'y': 1}, {'x': 4, 'y': 1}, {'x': 3, 'y': 1}, {'x': 2, 'y': 1}, {'x': 2, 'y': 2}, {'x': 2, 'y': 3}], 'head': {'x': 10, 'y': 2}, 'length': 14, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_tfVDkxJgKwJrHPQ6yhrWHxBD', 'name': 'ricksnek', 'latency': '101', 'health': 85, 'body': [{'x': 4, 'y': 8}, {'x': 4, 'y': 7}, {'x': 4, 'y': 6}, {'x': 4, 'y': 5}, {'x': 5, 'y': 5}, {'x': 5, 'y': 6}, {'x': 6, 'y': 6}, {'x': 7, 'y': 6}, {'x': 8, 'y': 6}, {'x': 8, 'y': 5}, {'x': 7, 'y': 5}, {'x': 7, 'y': 4}, {'x': 6, 'y': 4}], 'head': {'x': 4, 'y': 8}, 'length': 13, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_4HcJFdfSH9vcPMPRtX7Fy4WB', 'name': 'Nightwing', 'latency': '121', 'health': 60, 'body': [{'x': 7, 'y': 7}, {'x': 6, 'y': 7}, {'x': 6, 'y': 8}, {'x': 6, 'y': 9}, {'x': 6, 'y': 10}, {'x': 7, 'y': 10}, {'x': 8, 'y': 10}, {'x': 9, 'y': 10}, {'x': 9, 'y': 9}], 'head': {'x': 7, 'y': 7}, 'length': 9, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_fSqKx9mk7hw9qQ6y7gbpdWT3', 'name': 'danger-noodle', 'latency': '15', 'health': 43, 'body': [{'x': 2, 'y': 0}, {'x': 1, 'y': 0}, {'x': 1, 'y': 1}, {'x': 1, 'y': 2}, {'x': 1, 'y': 3}], 'head': {'x': 2, 'y': 0}, 'length': 5, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 0, 'y': 8}, {'x': 10, 'y': 7}, {'x': 0, 'y': 4}], 'hazards': []}, 'you': {'id': 'gs_4HcJFdfSH9vcPMPRtX7Fy4WB', 'name': 'Nightwing', 'latency': '121', 'health': 60, 'body': [{'x': 7, 'y': 7}, {'x': 6, 'y': 7}, {'x': 6, 'y': 8}, {'x': 6, 'y': 9}, {'x': 6, 'y': 10}, {'x': 7, 'y': 10}, {'x': 8, 'y': 10}, {'x': 9, 'y': 10}, {'x': 9, 'y': 9}], 'head': {'x': 7, 'y': 7}, 'length': 9, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +game_state = {'game': {'id': '6737333a-2a0c-41d1-b45e-72d60caf6f1f', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 3, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_pRdJRXch6bvRtbVfgvRTt7YY', 'name': 'Lispy', 'latency': '10', 'health': 99, 'body': [{'x': 9, 'y': 8}, {'x': 10, 'y': 8}, {'x': 10, 'y': 9}, {'x': 9, 'y': 9}], 'head': {'x': 9, 'y': 8}, 'length': 4, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_yvCT6PgMSchgXCXQGYvXGkVY', 'name': 'InGodWeRust', 'latency': '29', 'health': 99, 'body': [{'x': 7, 'y': 0}, {'x': 8, 'y': 0}, {'x': 9, 'y': 0}, {'x': 9, 'y': 1}], 'head': {'x': 7, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_Hq9jP7MwhgpQ34mfS3vdkB3Q', 'name': 'ricksnek', 'latency': '177', 'health': 97, 'body': [{'x': 1, 'y': 8}, {'x': 2, 'y': 8}, {'x': 2, 'y': 9}], 'head': {'x': 1, 'y': 8}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_YtDRmKJdmMXMCCGQ89MRJm8Y', 'name': 'colin', 'latency': '29', 'health': 97, 'body': [{'x': 2, 'y': 3}, {'x': 2, 'y': 2}, {'x': 1, 'y': 2}], 'head': {'x': 2, 'y': 3}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#6600ff', 'head': 'all-seeing', 'tail': 'weight'}}, {'id': 'gs_QvYJ8Mkb7MgMKQPMvRGFkWXR', 'name': 'Nightwing', 'latency': '78', 'health': 99, 'body': [{'x': 0, 'y': 7}, {'x': 0, 'y': 6}, {'x': 1, 'y': 6}, {'x': 1, 'y': 5}], 'head': {'x': 0, 'y': 7}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_SXSMyXgYqx8fy6T6bHq3Qv7D', 'name': 'viper', 'latency': '25', 'health': 97, 'body': [{'x': 5, 'y': 6}, {'x': 5, 'y': 7}, {'x': 5, 'y': 8}], 'head': {'x': 5, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#e04c07', 'head': 'missile', 'tail': 'nr-booster'}}, {'id': 'gs_MRYqXdQRJVPHm3d67Rhc49GK', 'name': 'tsnake', 'latency': '18', 'health': 97, 'body': [{'x': 5, 'y': 2}, {'x': 6, 'y': 2}, {'x': 6, 'y': 1}], 'head': {'x': 5, 'y': 2}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#ff66cc', 'head': 'scarf', 'tail': 'mystic-moon'}}, {'id': 'gs_fmW8p7d9Y4ttXHQWQMGWTMRH', 'name': 'danger-noodle', 'latency': '15', 'health': 97, 'body': [{'x': 8, 'y': 7}, {'x': 9, 'y': 7}, {'x': 9, 'y': 6}], 'head': {'x': 8, 'y': 7}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 0, 'y': 8}, {'x': 0, 'y': 2}, {'x': 4, 'y': 10}, {'x': 4, 'y': 0}, {'x': 10, 'y': 4}, {'x': 5, 'y': 5}], 'hazards': []}, 'you': {'id': 'gs_QvYJ8Mkb7MgMKQPMvRGFkWXR', 'name': 'Nightwing', 'latency': '78', 'health': 99, 'body': [{'x': 0, 'y': 7}, {'x': 0, 'y': 6}, {'x': 1, 'y': 6}, {'x': 1, 'y': 5}], 'head': {'x': 0, 'y': 7}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +game_state={'game': {'id': '70055471-751e-469e-b03a-62c4ffd4b565', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 33, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_XYmQ7dJVccqMVVCTp9t4w9cM', 'name': 'Lispy', 'latency': '30', 'health': 89, 'body': [{'x': 7, 'y': 8}, {'x': 8, 'y': 8}, {'x': 8, 'y': 7}, {'x': 7, 'y': 7}, {'x': 6, 'y': 7}, {'x': 6, 'y': 6}, {'x': 6, 'y': 5}, {'x': 7, 'y': 5}], 'head': {'x': 7, 'y': 8}, 'length': 8, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_r9hg6mtXWWdwBSYSb3hm763G', 'name': 'InGodWeRust', 'latency': '23', 'health': 75, 'body': [{'x': 5, 'y': 8}, {'x': 4, 'y': 8}, {'x': 3, 'y': 8}, {'x': 2, 'y': 8}, {'x': 1, 'y': 8}, {'x': 1, 'y': 7}], 'head': {'x': 5, 'y': 8}, 'length': 6, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_jYvDkwPMbJHHhfrhpccyXRgC', 'name': 'ricksnek', 'latency': '113', 'health': 83, 'body': [{'x': 3, 'y': 6}, {'x': 4, 'y': 6}, {'x': 4, 'y': 5}, {'x': 4, 'y': 4}, {'x': 4, 'y': 3}], 'head': {'x': 3, 'y': 6}, 'length': 5, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_wb6TJ9YbYRhm4CymSj3mSqrM', 'name': 'Nightwing', 'latency': '49', 'health': 95, 'body': [{'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 1, 'y': 10}, {'x': 0, 'y': 10}, {'x': 0, 'y': 9}], 'head': {'x': 5, 'y': 10}, 'length': 7, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_PP7MTmjVSFtJqqddwChthyGD', 'name': 'danger-noodle', 'latency': '15', 'health': 67, 'body': [{'x': 9, 'y': 6}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}], 'head': {'x': 9, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 6, 'y': 10}], 'hazards': []}, 'you': {'id': 'gs_wb6TJ9YbYRhm4CymSj3mSqrM', 'name': 'Nightwing', 'latency': '49', 'health': 95, 'body': [{'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 1, 'y': 10}, {'x': 0, 'y': 10}, {'x': 0, 'y': 9}], 'head': {'x': 5, 'y': 10}, 'length': 7, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +game_state = {'game': {'id': '9043a07a-ce19-4114-a22c-b99f2df9c52a', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 283, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_cb34P3qtR3gMJpmRfm6xPdmW', 'name': 'InGodWeRust', 'latency': '36', 'health': 99, 'body': [{'x': 5, 'y': 2}, {'x': 5, 'y': 3}, {'x': 6, 'y': 3}, {'x': 7, 'y': 3}, {'x': 7, 'y': 2}, {'x': 8, 'y': 2}, {'x': 8, 'y': 1}, {'x': 8, 'y': 0}, {'x': 7, 'y': 0}, {'x': 6, 'y': 0}, {'x': 5, 'y': 0}, {'x': 4, 'y': 0}, {'x': 3, 'y': 0}, {'x': 2, 'y': 0}, {'x': 2, 'y': 1}, {'x': 2, 'y': 2}, {'x': 1, 'y': 2}, {'x': 1, 'y': 1}, {'x': 0, 'y': 1}, {'x': 0, 'y': 2}, {'x': 0, 'y': 3}, {'x': 0, 'y': 4}, {'x': 0, 'y': 5}, {'x': 1, 'y': 5}, {'x': 1, 'y': 4}, {'x': 1, 'y': 3}, {'x': 2, 'y': 3}, {'x': 3, 'y': 3}], 'head': {'x': 5, 'y': 2}, 'length': 28, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_FpTKyQdH33y8XkkyJCrKwWj9', 'name': 'Nightwing', 'latency': '166', 'health': 78, 'body': [{'x': 3, 'y': 4}, {'x': 4, 'y': 4}, {'x': 5, 'y': 4}, {'x': 6, 'y': 4}, {'x': 6, 'y': 5}, {'x': 7, 'y': 5}, {'x': 8, 'y': 5}, {'x': 9, 'y': 5}, {'x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 10, 'y': 9}, {'x': 10, 'y': 10}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}], 'head': {'x': 3, 'y': 4}, 'length': 23, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}], 'food': [{'x': 1, 'y': 0}, {'x': 5, 'y': 1}], 'hazards': []}, 'you': {'id': 'gs_FpTKyQdH33y8XkkyJCrKwWj9', 'name': 'Nightwing', 'latency': '166', 'health': 78, 'body': [{'x': 3, 'y': 4}, {'x': 4, 'y': 4}, {'x': 5, 'y': 4}, {'x': 6, 'y': 4}, {'x': 6, 'y': 5}, {'x': 7, 'y': 5}, {'x': 8, 'y': 5}, {'x': 9, 'y': 5}, {'x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 10, 'y': 9}, {'x': 10, 'y': 10}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}], 'head': {'x': 3, 'y': 4}, 'length': 23, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} +game_state = {'game': {'id': 'a0246956-0611-497d-a852-635b3bba4ee8', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 91, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_XjXd4v4w8yRYfTXpqYMpVjmX', 'name': 'Nightwing', 'latency': '500', 'health': 88, 'body': [{'x': 6, 'y': 3}, {'x': 6, 'y': 2}, {'x': 6, 'y': 1}, {'x': 7, 'y': 1}, {'x': 8, 'y': 1}, {'x': 9, 'y': 1}, {'x': 9, 'y': 2}, {'x': 9, 'y': 3}], 'head': {'x': 6, 'y': 3}, 'length': 8, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_F7y7pM3vptdhTJhQd8YXYh3b', 'name': 'InGodWeRust', 'latency': '26', 'health': 96, 'body': [{'x': 1, 'y': 8}, {'x': 1, 'y': 7}, {'x': 0, 'y': 7}, {'x': 0, 'y': 6}, {'x': 0, 'y': 5}, {'x': 1, 'y': 5}, {'x': 2, 'y': 5}, {'x': 3, 'y': 5}, {'x': 4, 'y': 5}, {'x': 5, 'y': 5}, {'x': 6, 'y': 5}, {'x': 6, 'y': 6}], 'head': {'x': 1, 'y': 8}, 'length': 12, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_9PptFR4y7BmP6cX9rrRTFytP', 'name': 'ricksnek', 'latency': '99', 'health': 96, 'body': [{'x': 8, 'y': 9}, {'x': 9, 'y': 9}, {'x': 9, 'y': 8}, {'x': 10, 'y': 8}, {'x': 10, 'y': 7}, {'x': 9, 'y': 7}, {'x': 8, 'y': 7}, {'x': 7, 'y': 7}, {'x': 6, 'y': 7}], 'head': {'x': 8, 'y': 9}, 'length': 9, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}], 'food': [{'x': 1, 'y': 9}], 'hazards': []}, 'you': {'id': 'gs_XjXd4v4w8yRYfTXpqYMpVjmX', 'name': 'Nightwing', 'latency': '500', 'health': 88, 'body': [{'x': 6, 'y': 3}, {'x': 6, 'y': 2}, {'x': 6, 'y': 1}, {'x': 7, 'y': 1}, {'x': 8, 'y': 1}, {'x': 9, 'y': 1}, {'x': 9, 'y': 2}, {'x': 9, 'y': 3}], 'head': {'x': 6, 'y': 3}, 'length': 8, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game_state = {'game': {'id': 'b4095e43-09b4-47b8-bac5-423bf1737dea', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 156, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_ffjXg8HCP4KxXfJBKCxwYVTF', 'name': 'Nightwing', 'latency': '20', 'health': 99, 'body': [{'x': 9, 'y': 5}, {'+x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 9, 'y': 8}, {'x': 8, 'y': 8}, {'x': 7, 'y': 8}, {'x': 6, 'y': 8}, {'x': 6, 'y': 7}, {'x': 6, 'y': 6}, {'x': 6, 'y': 5}, {'x': 5, 'y': 5}, {'x': 5, 'y': 6}, {'x': 5, 'y': 7}, {'x': 5, 'y': 8}, {'x': 5, 'y': 9}, {'x': 5, 'y': 10}], 'head': {'x': 9, 'y': 5}, 'length': 20, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_CMvfTppGY9jfWTSr4Xq6PWwV', 'name': 'InGodWeRust', 'latency': '24', 'health': 93, 'body': [{'x': 8, 'y': 2}, {'x': 8, 'y': 3}, {'x': 9, 'y': 3}, {'x': 9, 'y': 2}, {'x': 10, 'y': 2}, {'x': 10, 'y': 1}, {'x': 10, 'y': 0}, {'x': 9, 'y': 0}, {'x': 8, 'y': 0}, {'x': 7, 'y': 0}, {'x': 7, 'y': 1}, {'x': 7, 'y': 2}, {'x': 7, 'y': 3}, {'x': 7, 'y': 4}, {'x': 6, 'y': 4}, {'x': 6, 'y': 3}, {'x': 5, 'y': 3}], 'head': {'x': 8, 'y': 2}, 'length': 17, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}], 'food': [{'x': 1, 'y': 7}], 'hazards': []}, 'you': {'id': 'gs_ffjXg8HCP4KxXfJBKCxwYVTF', 'name': 'Nightwing', 'latency': '20', 'health': 99, 'body': [{'x': 9, 'y': 5}, {'x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 9, 'y': 8}, {'x': 8, 'y': 8}, {'x': 7, 'y': 8}, {'x': 6, 'y': 8}, {'x': 6, 'y': 7}, {'x': 6, 'y': 6}, {'x': 6, 'y': 5}, {'x': 5, 'y': 5}, {'x': 5, 'y': 6}, {'x': 5, 'y': 7}, {'x': 5, 'y': 8}, {'x': 5, 'y': 9}, {'x': 5, 'y': 10}], 'head': {'x': 9, 'y': 5}, 'length': 20, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game_state = {'game': {'id': '7c824fdd-cba6-40a2-b48f-a3c28de68d55', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 2, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_pqhxF7VjWMkrcxfJfTdbRRGP', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 8, 'y': 10}, {'x': 8, 'y': 9}, {'x': 9, 'y': 9}, {'x': 9, 'y': 9}], 'head': {'x': 8, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_Wmqy7TyyVbBbrSj39vCmkWdC', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 2, 'y': 10}, {'x': 2, 'y': 9}, {'x': 1, 'y': 9}, {'x': 1, 'y': 9}], 'head': {'x': 2, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_dfmjdmQfGG7kcYSY9GJBKk8H', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 8, 'y': 0}, {'x': 8, 'y': 1}, {'x': 9, 'y': 1}, {'x': 9, 'y': 1}], 'head': {'x': 8, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_MtXTyh34DfwdCgphMxhMbqy8', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 0, 'y': 2}, {'x': 0, 'y': 1}, {'x': 1, 'y': 1}, {'x': 1, 'y': 1}], 'head': {'x': 0, 'y': 2}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_ffHgjxxHQjHV9Mbt67FYhCvb', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 9, 'y': 5}, {'x': 9, 'y': 5}], 'head': {'x': 10, 'y': 4}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_jP7yypx9XkYGFHrjR96pXRy4', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 4, 'y': 0}, {'x': 4, 'y': 1}, {'x': 5, 'y': 1}, {'x': 5, 'y': 1}], 'head': {'x': 4, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}, {'id': 'gs_hgwtX9HJ8r4pfSQS3kbf6P6P', 'name': 'Nightwing', 'latency': '354', 'health': 100, 'body': [{'x': 4, 'y': 10}, {'x': 5, 'y': 10}, {'x': 5, 'y': 9}, {'x': 5, 'y': 9}], 'head': {'x': 4, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_cY7kMdHXjhtWtbD67g8x8CPH', 'name': 'Hungry Bot', 'latency': '1', 'health': 100, 'body': [{'x': 0, 'y': 6}, {'x': 0, 'y': 5}, {'x': 1, 'y': 5}, {'x': 1, 'y': 5}], 'head': {'x': 0, 'y': 6}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00cc00', 'head': 'alligator', 'tail': 'alligator'}}], 'food': [{'x': 5, 'y': 5}], 'hazards': []}, 'you': {'id': 'gs_hgwtX9HJ8r4pfSQS3kbf6P6P', 'name': 'Nightwing', 'latency': '354', 'health': 100, 'body': [{'x': 4, 'y': 10}, {'x': 5, 'y': 10}, {'x': 5, 'y': 9}, {'x': 5, 'y': 9}], 'head': {'x': 4, 'y': 10}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} game = Battlesnake(game_state) + +print("STARTING POSITION") game.display_board() + +# l = game.flood_fill(game.my_id, confined_area="left") +# u = game.flood_fill(game.my_id, confined_area="up") +# r = game.flood_fill(game.my_id, confined_area="right") +# print(l, u, r) next_move = game.minimax_move() -print(next_move) \ No newline at end of file +print(next_move) +# +# import cProfile, pstats +# profiler = cProfile.Profile() +# profiler.enable() +# game.minimax_move() +# profiler.disable() +# stats = pstats.Stats(profiler).sort_stats('tottime') +# stats.print_stats() + +# import timeit +# start = timeit.default_timer() +# print("The start time is :", start) +# next_move = game.flood_fill(game.my_id, confined_area="left") +# print("The difference of time is :", +# timeit.default_timer() - start) \ No newline at end of file diff --git a/helper_functions.py b/helper_functions.py deleted file mode 100644 index 644d635f..00000000 --- a/helper_functions.py +++ /dev/null @@ -1,79 +0,0 @@ -import copy -import time -import numpy as np -import typing - - -def look_ahead(head, move): - """Given a possible move, return the snake's new coordinates if it were headed that way""" - snake_head = head.copy() - if move == "up": - snake_head["y"] += 1 - if move == "down": - snake_head["y"] -= 1 - if move == "left": - snake_head["x"] -= 1 - if move == "right": - snake_head["x"] += 1 - return snake_head - -def simulate_move(game_state, move, snake_id): - """Given a game_state variable, simulate a board with a given move for a given snake""" - game_state2 = game_state - all_snakes = game_state2["board"]["snakes"] - head = [snake for snake in all_snakes if snake["id"] == snake_id][0]["head"] - new_head = look_ahead(head, move) - - snake_index = [snake["id"] for snake in all_snakes].index(snake_id) - game_state2["board"]["snakes"][snake_index]["body"] = [new_head] + game_state2["board"]["snakes"][snake_index]["body"][:-1] # Remove tail - if snake_id == game_state2["you"]["id"]: - game_state2["you"]["body"] = [new_head] + game_state2["you"]["body"][:-1] # Remove tail - game_state2["you"]["head"] = new_head - return game_state2 - -def calc_manhattan_distance(start, end): - """Return the Manhattan distance for two positions""" - start = (start["x"], start["y"]) - end = (end["x"], end["y"]) - return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) - -def flood_fill(game_state, risk_averse=True): - """Recursive function to get the total area of the current fill selection""" - clock_in = time.time_ns() - board_width = game_state["board"]["width"] - board_height = game_state["board"]["height"] - my_head = game_state["you"]["head"] - my_length = game_state["you"]["length"] - - # Make an (l x w) array and simulate the board - board = np.empty((board_width, board_height), dtype=" head["x"]: # Neck is right of head, don't move right @@ -106,226 +179,515 @@ def get_obvious_moves(self, game_state=None, snake_id=None, head=None, neck=None possible_moves.discard("up") # Prevent snake from colliding with other snakes - if not allow_suicide: # Unless the snake is willing to die - for opp_snake in self.all_snakes: - for move in possible_moves.copy(): # Remaining moves - # If the potential move hits a snake's body, it's invalid (but not the tail since it moves forward) - if look_ahead(head, move) in opp_snake["body"][:-1]: - possible_moves.discard(move) - # Avoid possible collisions with any future moves an opponent snake can make + for opp_snake_id, opp_snake in self.all_snakes_dict.items(): + for move in possible_moves.copy(): # Remaining moves + possible_hit = self.look_ahead(head, move) + # If the potential move hits a snake's body, it's invalid (exclude the tail since it moves forward) + if possible_hit in opp_snake["body"][:-1] and possible_hit != self.my_head: + possible_moves.discard(move) + # If the snake is risk-averse, avoid any chance of head-on collisions only if our snake is shorter + elif snake_id != opp_snake_id and length <= opp_snake["length"] \ + and self.manhattan_distance(possible_hit, opp_snake["head"]) == 1: + risky_moves.append(move) if risk_averse: - snake_length = [snake for snake in self.all_snakes if snake["id"] == snake_id][0]["length"] - # Only if the snake in question is shorter - if opp_snake["id"] != snake_id and snake_length < opp_snake["length"]: - opp_moves = [look_ahead(opp_snake["head"], move2) for move2 in ["up", "down", "left", "right"]] - if look_ahead(head, move) in opp_moves: - possible_moves.discard(move) - - print(f"Identified obvious moves {list(possible_moves)} in {(time.time_ns() - clock_in) // 1000000} ms") + possible_moves.discard(move) + + if sort_by_dist_to is not None: + head2 = self.all_snakes_dict[sort_by_dist_to]["head"] + possible_moves = sorted(possible_moves, # This converts a set to a list anyway + key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) + elif sort_by_peripheral: + possible_moves = sorted(possible_moves, # This converts a set to a list anyway + key=lambda move2: self.flood_fill(snake_id, confined_area=move2), + reverse=True) + else: + possible_moves = list(possible_moves) + + # De-prioritise any risky moves and send them to the back + if len(risky_moves) > 0: + for risky in risky_moves: + if risky in possible_moves: + possible_moves.append(possible_moves.pop(possible_moves.index(risky))) + + logging.info(f"Identified obvious moves {list(possible_moves)} in " + f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") return list(possible_moves) - def minimax_move(self, risk_averse=True): + def minimax_move(self): + """Let's run the minimax algorithm with alpha-beta pruning!""" # Compute the best score of each move using the minimax algorithm with alpha-beta pruning - minimax_score, best_move = self.minimax(self.game_state, depth=3, alpha=-np.inf, beta=np.inf, maximising_snake=True) + if self.turn < 5: + search_depth = 1 + elif len(self.opponents) > 6: + search_depth = 2 # TODO should be risk-averse + elif len(self.opponents) >= 4: + search_depth = 4 + else: + search_depth = self.minimax_search_depth + _, best_move = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) return best_move - def is_game_over(self, game_state=None, maximising_snake=True): - """Return True if our snake won or lost, False otherwise""" - # Just if you want to simulate moves for a different game state - input_game = game_state if game_state is not None else self.game_state - all_snakes = input_game["board"]["snakes"] - if maximising_snake: # This means it's our turn - snake_heads = [input_game["you"]["head"]] - snake_lengths = [input_game["you"]["length"]] - snake_ids = [self.my_id] - else: # Opponent's turn - snake_heads = [snake["head"] for snake in all_snakes if snake["id"] != self.my_id] - snake_lengths = [snake["length"] for snake in all_snakes if snake["id"] != self.my_id] - snake_ids = [snake["id"] for snake in all_snakes if snake["id"] != self.my_id] - - snakes_alive = [True] * len(snake_heads) # Initialise a boolean list for each snake - for i in range(len(snake_heads)): - coords = snake_heads[i] + def is_game_over(self, for_snake_id=None, depth=None): + """Return True if the game ended for our snake, False otherwise. Use for_snake_id to return a boolean for a + specific snake's status (can be one or multiple).""" + snake_monitor = {} # A dictionary for each snake showing whether they're alive + for snake_id, snake in self.all_snakes_dict.items(): + snake_monitor[snake_id] = True + + head = snake["head"] # Prevent snake from moving out of bounds - if coords["x"] < 0 or coords["x"] >= self.board_height: - snakes_alive[i] = False - if coords["y"] < 0 or coords["y"] >= self.board_width: - snakes_alive[i] = False - - # Prevent snake from colliding with other snakes - for opp_snake in all_snakes: - if opp_snake["id"] != snake_ids[i]: + if head["x"] < 0 or head["x"] >= self.board_height: + snake_monitor[snake_id] = False + if head["y"] < 0 or head["y"] >= self.board_width: + snake_monitor[snake_id] = False + + # Skip this if we're at the beginning of the game when all snakes are still coiled up + if not (snake["length"] == 3 and snake["body"][-2] == snake["body"][-1]): + # Prevent snake from colliding with any other snakes + for opp_snake_id, opp_snake in self.all_snakes_dict.items(): + # Depending on the depth, exclude the tail if the turn technically isn't over (all players need to + # have made a move) + check_body = opp_snake["body"][1:-1] if depth % 2 == 1 else opp_snake["body"][1:] + # If the potential move hits a snake's body, it's invalid - if coords in opp_snake["body"][1:]: - snakes_alive[i] = False - # If the potential move hits a snake's head, it's invalid only if it's shorter - if coords == opp_snake["body"][0] and snake_lengths[i] <= opp_snake["length"]: - snakes_alive[i] = False - - return True if sum(snakes_alive) == 0 else False - - def heuristic(self, game_state): - return flood_fill(game_state, risk_averse=True) - - def store_board_positions(self, game_state): - """Things get confusing when we're simulating snake positions over and over. Making shallow copies of game_state - variables to simulate a new position causes a loss of information in the original board, and making deep copies - adds too much to the runtime. - - Solution: this function can keep a temporary copy of all the positions of all snakes and can be re-inserted into - the game_state variable at any point to "restore" the original board.""" - # Grab our position - copy_our_pos = game_state["you"]["body"].copy() - - # Grab opponents' positions - # We need the index of each opponent snake in the game_state's "snakes" field for later use - num_snakes = len(game_state["board"]["snakes"]) - copy_opps_bodies = [game_state["board"]["snakes"][index]["body"] for index in range(num_snakes)] - copy_opps_pos = {index: copy_opps_bodies[index] for index in range(num_snakes)} - return {"you": copy_our_pos, "snakes": copy_opps_pos} + if head in check_body: + snake_monitor[snake_id] = False + # If the potential move hits a snake's head, it's invalid only if our snake is shorter + if snake_id != opp_snake_id: + if head == opp_snake["head"] and snake["length"] <= opp_snake["length"]: + snake_monitor[snake_id] = False + + # Game is over if there's only one snake remaining or if our snake died + game_over = True if (sum(snake_monitor.values()) == 1 or not snake_monitor[self.my_id]) else False + + # See if a specific snake is alive or not + if isinstance(for_snake_id, (list, tuple)): + snake_still_alive = [snake_monitor[snake_id] for snake_id in for_snake_id] + else: + snake_still_alive = snake_monitor[self.my_id] if for_snake_id is None else snake_monitor[for_snake_id] - @staticmethod - def restore_board_positions(game_state, snake_position_dict): - """Goes hand-in-hand with the store_board_position function""" - # Restore our positions - copy_our_pos = snake_position_dict["you"] - game_state["you"]["body"] = copy_our_pos - game_state["you"]["head"] = copy_our_pos[0] - game_state["you"]["length"] = len(copy_our_pos) - - # Replace all other snakes' positions - copy_opps_pos = snake_position_dict["snakes"] - for index, body in copy_opps_pos.items(): - game_state["board"]["snakes"][index]["body"] = copy_opps_pos[index] - game_state["board"]["snakes"][index]["head"] = copy_opps_pos[index][0] - game_state["board"]["snakes"][index]["length"] = len(copy_opps_pos[index]) - return game_state - - def minimax(self, game_state, depth, alpha, beta, maximising_snake): + return game_over, snake_still_alive + + def heuristic(self, depth_number): + """Let's figure out a way to evaluate the current board for our snake :) + + Should be a function of: + - Maximising available space for our snake (flood fill) + - Minimising available space for enemy snakes + - Distance to food + - How far down our game tree we went in our minimax search + """ + # Determine how many layers deep in the game tree we are + layers_deep = self.minimax_search_depth - depth_number + + # If an opponent snake dies :) + + # Determine available space via flood fill + available_space = self.flood_fill(self.my_id, risk_averse=True) + + # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in + # our vicinity) + if len(self.opponents) <= 4 \ + and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 4 \ + and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + self.peripheral_dim = 4 + available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") + else: + available_enemy_space = 0 + + if 2 >= len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + dist_to_enemy = self.dist_from_enemies()[0] + else: + dist_to_enemy = 0 + + # Determine the closest safe distance to food + dist_food = self.dist_to_nearest_food() + + # Are we in the centre of the board? Maximise control + centre = range(self.board_width // 2 - 2, self.board_width // 2 + 3) + in_centre = (self.my_head["x"] in centre and self.my_head["x"] in centre) and (len(self.opponents) <= 2) + + # Heuristic formula + space_weight = 0.5 + enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 + food_weight = 75 + depth_weight = 25 + length_weight = 150 + centre_control_weight = 10 + aggression_weight = 2500 + logging.info(f"Available space: {available_space}") + logging.info(f"Distance to nearest enemy: {dist_to_enemy}") + logging.info(f"Distance to nearest food: {dist_food}") + logging.info(f"Layers deep in search tree: {layers_deep}") + logging.info(f"Available enemy space: {available_enemy_space}") + logging.info(f"In centre: {in_centre}") + logging.info(f"Length: {self.my_length}") + h = (available_space * space_weight) + \ + (food_weight / (dist_food + 1)) + \ + (layers_deep * depth_weight) + \ + (self.my_length * length_weight) + \ + in_centre * centre_control_weight + \ + (enemy_restriction_weight / (available_enemy_space + 1)) + \ + aggression_weight / (dist_to_enemy + 1) + + return h + + def flood_fill(self, snake_id, confined_area=None, risk_averse=False): + """Recursive function to get the total area of the current fill selection. Basically, count how many £ symbols + we can fill while avoiding any $, O, and X symbols (obstacles). Confined_area tells the function to do flood + fill only on one side of the snake (should be either left/right/up/down).""" + head = self.all_snakes_dict[snake_id]["head"] + + # Assume we're doing flood fill for our snake + if snake_id == self.my_id: + board = copy.deepcopy(self.board) + # Let's try to avoid any squares that our enemy can go to + if risk_averse: + threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] + for threat in threats: + x, y = threat["x"], threat["y"] + avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in avoid_sq: + if not (n[0] == head["x"] and n[1] == head["y"]) and \ + (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): + board[n[0]][n[1]] = "X" + # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) + else: + board = np.full((self.board_width, self.board_height), " ") + for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): + board[square["x"], square["y"]] = "£" if num == 0 else "O" + for other_id, other_snake in self.all_snakes_dict.items(): + if other_id != snake_id: + for num, other_square in enumerate(other_snake["body"]): + board[other_square["x"], other_square["y"]] = "$" if num == 0 else "X" + + if confined_area is not None: + xs, ys, head = self.peripheral_vision(snake_id, confined_area) + board = board[xs[0]:xs[1], ys[0]:ys[1]] + # if -1 == head["x"]: + # self.peripheral_vision(snake_id, confined_area) + + def fill(x, y, board, initial_square): + if board[x][y] in ["X", "$"]: # Snakes or hazards + return + if board[x][y] in ["O"]: # Us + return + if board[x][y] == "£" and not initial_square: # Already filled + return + board[x][y] = "£" + neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in neighbours: + if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): + fill(n[0], n[1], board, initial_square=False) + + fill(head["x"], head["y"], board, initial_square=True) + filled = sum((row == "£").sum() for row in board) + return filled - 1 if filled > 0 else filled # Exclude the head, but cannot ever be negative + + def dist_to_nearest_food(self): + """Return the Manhattan distance to the nearest food for our snake""" + best_dist = np.inf + for food in self.food: + dist = self.manhattan_distance(food, self.my_head) + if dist < best_dist: + best_dist = dist + return best_dist + + def dist_from_enemies(self): + """To gauge how in danger we are, return a list of Manhattan distances to all snakes""" + return sorted([self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values()]) + + def closest_enemy(self): + """Return the nearest enemy snake""" + return sorted(self.opponents.keys(), + key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]))[0] + + def simulate_move(self, move, snake_id, evaluate_deaths=False): + """Create a new Battlesnake instance with a simulated move for a given snake""" + clock_in = time.time_ns() + new_game = self.__copy__() + + # Simulate the new head position + old_head = self.all_snakes_dict[snake_id]["head"] + new_head = self.look_ahead(old_head, move) + + # Insert the simulated snake position into the new instance + new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] + new_game.all_snakes_dict[snake_id]["head"] = new_head + new_game.all_snakes_dict[snake_id]["health"] -= 1 + # Repeat for our snake's specific attributes + if snake_id == self.my_id: + new_game.my_body = [new_head] + new_game.my_body[:-1] + new_game.my_head = new_head + new_game.my_health -= 1 + + # If food was consumed, this elongates the snake from the tail and restores health + if new_head in self.food: + new_game.all_snakes_dict[snake_id]["length"] += 1 + new_game.all_snakes_dict[snake_id]["health"] = 100 + new_game.all_snakes_dict[snake_id]["body"] += [new_game.all_snakes_dict[snake_id]["body"][-1]] + new_game.food = [food for food in self.food if not (food["x"] == new_head["x"] and food["y"] == new_head["y"])] + # Repeat for our snake specifically + if snake_id == self.my_id: + new_game.my_length += 1 + new_game.my_health = 100 + new_game.my_body += [new_game.my_body[-1]] + + # Check if any snakes died from this simulated move and remove them from the game + if evaluate_deaths: + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] + count_heads = Counter(all_heads) + butt_heads = [k for k, v in count_heads.items() if v > 1] + for butt_head in butt_heads: + overlapping_snakes = np.array([ + (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() + if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) + ]) + # Special cases where the snake committed suicide and also killed our snake => don't remove + if self.my_id not in overlapping_snakes[:, 0]: + lengths = overlapping_snakes[:, 1].astype(int) + indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() + if len(indices_largest_snakes) > 1: + winner_id = None + else: + winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] + + for rm_id in overlapping_snakes[:, 0]: + if rm_id != winner_id: + new_game.all_snakes_dict.pop(rm_id) + + new_game.update_board() + logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + return new_game + + def minimax(self, depth, alpha, beta, maximising_snake): """Implement the minimax algorithm with alpha-beta pruning - :param game_state: A fresh copy of the game state dictionary (not from self.game_state in case we need to - simulate different situations) :param depth: :param alpha: :param beta: :param maximising_snake: :return: """ - global counter - counter += 1 + if depth != self.minimax_search_depth: + # Check if our snake died + game_over, still_alive = self.is_game_over(for_snake_id=self.my_id, depth=depth) + if not still_alive: + logging.info("Our snake died...") + return -1e6 + (self.minimax_search_depth - depth), None # Reward slower deaths + # Otherwise, if our snake is ALIVE and is the winner :) + elif game_over: + logging.info("OUR SNAKE WON") + return 1e6, None # At the bottom of the decision tree or if we won/lost the game - if self.is_game_over(game_state, maximising_snake): - print("=" * 50) - print(f"Heuristic = -100 at terminal node of depth = {depth}") - return -100, None if depth == 0: - heur = self.heuristic(game_state) - print("=" * 50) - print(f"Heuristic = {heur} at terminal node of depth = {depth}") - return heur, None + logging.info("=" * 50) + logging.info(f"DEPTH = {depth}") + heuristic = self.heuristic(depth_number=depth) + logging.info(f"Heuristic = {heuristic} at terminal node") + return heuristic, None # Our snake's turn if maximising_snake: + logging.info("=" * 50) + logging.info(f"DEPTH = {depth} OUR SNAKE") + logging.info(f"ALPHA = {alpha} | beta = {beta}") + clock_in = time.time_ns() - print("=" * 50) - print(f"DEPTH == {depth} OUR SNAKE") - possible_moves = self.get_obvious_moves(game_state, risk_averse=False) + possible_moves = self.get_obvious_moves( # If > 6 opponents, we'll do depth = 2 and risk_averse = True + self.my_id, risk_averse=(len(self.opponents) > 6), sort_by_peripheral=True) + if len(possible_moves) == 0: # RIP + possible_moves = ["down"] best_val, best_move = -np.inf, None - - # Store the positions of all snakes before making any board simulations - stored_snake_positions = self.store_board_positions(game_state) - for num, move in enumerate(possible_moves): - # Restore game state after the first simulation - if num > 0: - game_state = self.restore_board_positions(game_state, stored_snake_positions) + SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) - sim_game_state = simulate_move(game_state, move, self.my_id) + logging.info(f"{len(possible_moves)} CHILD NODES: VISITING {num + 1} OF {len(possible_moves)}") + logging.info(f"Running minimax for OUR SNAKE moving {move}") + SIMULATED_BOARD_INSTANCE.display_board() clock_in2 = time.time_ns() - print(f"Running Minimax for OUR SNAKE'S move {move} at depth = {depth} at counter = {counter}") - node_val, node_move = self.minimax(sim_game_state, depth - 1, alpha, beta, False) + node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, False) + + logging.info("=" * 50) + logging.info(f"BACK AT DEPTH = {depth} OUR SNAKE") + logging.info(f"ALPHA = {alpha} | beta = {beta}") # Update best score and best move if np.argmax([best_val, node_val]) == 1: - best_move = node_move if node_move is not None else move # None when it's a terminal node + best_move = move best_val = max(best_val, node_val) + old_alpha = alpha + alpha = max(alpha, best_val) - print("=" * 50) - print(f"BACK AT DEPTH = {depth}") - print(f"Heuristic = {best_val} and best move = {best_move} at our node at depth = {depth - 1} in {(time.time_ns() - clock_in2) // 1000000} ms") + logging.info(f"Updated ALPHA from {old_alpha} to {alpha}") + logging.info(f"Identified best move so far = {best_move} in {round((time.time_ns() - clock_in2) / 1000000, 3)} ms") # Check to see if we can prune - alpha = max(alpha, best_val) if alpha >= beta: - print("PRUNED!!!") + logging.info(f"PRUNED!!! Alpha = {alpha} >= Beta = {beta}") break - print(f"Minimax on our snake in {(time.time_ns() - clock_in) // 1000000} ms") + logging.info(f"FINISHED MINIMAX LAYER on our snake in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") return best_val, best_move - # Opponent snakes' turns + # Opponents' turns else: + logging.info("=" * 50) + logging.info(f"DEPTH = {depth} OPPONENT SNAKES") + logging.info(f"BETA = {beta} | alpha = {alpha}") + clock_in = time.time_ns() - print("=" * 50) - print(f"DEPTH == {depth} OPPONENT SNAKES") + # If there are multiple opponent snakes, search only for those within a reasonable distance of ours + if len(self.opponents) == 1: + search_within = self.board_width * self.board_height + elif len(self.opponents) <= 3: + search_within = self.board_width * 2 + elif len(self.opponents) < 6: + search_within = self.board_width // 2 + else: + search_within = self.board_width // 2 - 1 # Grab possible moves for all opponent snakes - all_opps_ids = [] - all_opps_moves = [] # Going to be a list of lists - for opp_snake in self.opponents: - opp_id = opp_snake["id"] - # But only within a reasonable distance of our own snake - if calc_manhattan_distance(self.my_head, opp_snake["head"]) < self.board_width // 2: - all_opps_moves.append(self.get_obvious_moves(game_state, snake_id=opp_id, allow_suicide=True)) - all_opps_ids.append(opp_id) - print(f"{len(all_opps_ids)} OPPONENT SNAKES within {self.board_width // 2} of us in {(time.time_ns() - clock_in) // 1000000} ms") - - # Store the positions of all snakes before making any board simulations - stored_snake_positions = self.store_board_positions(game_state) + opps_nearby = 0 # Counter for opponents in our vicinity + opps_moves = {} # Store possible moves for each snake id + for opp_id, opp_snake in self.opponents.items(): + opp_move = self.get_obvious_moves(opp_id, risk_averse=False, sort_by_dist_to=self.my_id) + if len(opp_move) == 0: # If the snake has no legal moves, move down and die + opp_move = ["down"] + + # Save time by only searching for snakes within close range + if self.manhattan_distance(self.my_head, opp_snake["head"]) < search_within: + opps_moves[opp_id] = opp_move # ALL OF THEM + opps_nearby += 1 + else: + if len(self.opponents) <= 5: + opps_moves[opp_id] = [opp_move[0]] + + logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {self.board_width // 2} " + f"squares of us in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") clock_in = time.time_ns() - possible_sim_states = [] + # If >= 3 board simulations, then randomly sample 3 of them based on how threatening the position is to our + # snake to cut down on runtime + all_opp_combos = list(itertools.product(*opps_moves.values())) + if len(all_opp_combos) > 2 and len(self.opponents) > 2: + logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") + all_opp_combos = all_opp_combos[:2] + elif len(all_opp_combos) > 3: + logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 3") + all_opp_combos = all_opp_combos[:3] + possible_movesets = [] + possible_sims = [] # Get all possible boards by simulating moves for each opponent snake, one at a time - all_opp_combos = list(itertools.product(*all_opps_moves)) - for num, move_combo in enumerate(all_opp_combos): - # Restore game state after the first simulation - if num > 0: - game_state = self.restore_board_positions(game_state, stored_snake_positions) - - sim_game_state = game_state.copy() - for snake_num, move in enumerate(move_combo): - sim_game_state = simulate_move(sim_game_state, move, all_opps_ids[snake_num]) - possible_sim_states.append(self.store_board_positions(sim_game_state)) + for move_combo in all_opp_combos: + SIMULATED_BOARD_INSTANCE = self.__copy__() + for num, move in enumerate(move_combo): + evaluate_flag = (num + 1 == len(move_combo)) + SIMULATED_BOARD_INSTANCE = SIMULATED_BOARD_INSTANCE.simulate_move( + move, list(opps_moves.keys())[num], evaluate_deaths=evaluate_flag) + # SIMULATED_BOARD_INSTANCE.display_board() + possible_sims.append(SIMULATED_BOARD_INSTANCE.__copy__()) possible_movesets.append(move_combo) - print(f"{len(possible_sim_states)} POSSIBLE SIMULATED BOARDS OF OPPONENT MOVES in {(time.time_ns() - clock_in) // 1000000} ms") + logging.info(f"SIMULATED {len(possible_sims)} POSSIBLE BOARDS OF OPPONENT MOVE COMBOS in " + f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") + + clock_in = time.time_ns() best_val, best_move = np.inf, None - game_state = self.restore_board_positions(game_state, stored_snake_positions) - for num, stored_sim in enumerate(possible_sim_states): + for num, SIMULATED_BOARD_INSTANCE in enumerate(possible_sims): + logging.info(f"{len(possible_sims)} CHILD NODES: VISITING {num + 1} OF {len(possible_sims)}") + logging.info(f"Running minimax for OPPONENT SNAKES moving {possible_movesets[num]}") + SIMULATED_BOARD_INSTANCE.display_board() clock_in2 = time.time_ns() - sim_state = self.restore_board_positions(game_state, stored_sim) - print(f"TESTING {possible_movesets[num]}") - Battlesnake(sim_state).display_board() - print(f"Running Minimax for OPPONENT SNAKES' moves at depth = {depth} at counter = {counter}") - node_val, node_move = self.minimax(sim_state, depth - 1, alpha, beta, True) + node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, True) + + logging.info("=" * 50) + logging.info(f"BACK AT DEPTH = {depth} OPPONENT SNAKES") + logging.info(f"BETA = {beta} | alpha = {alpha}") # Update best score and best move - best_val = min(best_val, node_val) if np.argmin([best_val, node_val]) == 1: - best_move = node_move if node_move is not None else possible_movesets[num] + best_move = possible_movesets[num] + best_val = min(best_val, node_val) + old_beta = beta + beta = min(beta, best_val) - print("=" * 50) - print(f"BACK AT DEPTH = {depth}") - print(f"Heuristic = {best_val} and best move = {best_move} at opponents' node at depth = {depth - 1} in {(time.time_ns() - clock_in2) // 1000000} ms") + logging.info(f"Updated BETA from {old_beta} to {beta}") + logging.info(f"Identified best move so far = {best_move} in {round((time.time_ns() - clock_in2) / 1000000, 3)} ms") # Check to see if we can prune - beta = min(beta, best_val) if beta <= alpha: - print("PRUNED!!!") + logging.info(f"PRUNED!!! Beta = {beta} <= Alpha = {alpha}") break - print(f"Minimax on opponents took {(time.time_ns() - clock_in) // 1000000} ms") - return best_val, best_move \ No newline at end of file + logging.info(f"FINISHED MINIMAX LAYER on opponents in {(time.time_ns() - clock_in) // 1000000} ms") + return best_val, best_move + +# def a_star_search(self, food_loc): +# """ +# Implement the A* search algorithm to find the shortest path to food +# """ +# head_loc = self.my_head.copy() +# open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet +# closed_list = [] # Contains the nodes which are examined +# timer = 0 +# +# while len(open_list) > 0: +# if timer > 300: +# return None, None +# +# current_node = min(open_list, key=lambda node: node.f) # The node with the smallest f value +# open_list.remove(current_node) # Remove from open list +# +# if current_node.location == food_loc: # Found the food! Stop search +# traceback = [food_loc] +# pointer = current_node +# while pointer.parent is not None: +# traceback.append(pointer.parent.location) +# pointer = pointer.parent +# return traceback, current_node.g +# +# # Identify all possible nodes to move to +# neighbour_nodes = [] +# possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) +# +# for possible_move in possible_moves: +# possible_node = Node(self.look_ahead(current_node.location, possible_move), parent=current_node) +# neighbour_nodes.append(possible_node) +# +# for possible_node in neighbour_nodes: # For each possible new node +# if possible_node.location in closed_list: # Skip if the node is in the closed list +# continue +# +# # Compute f value for the neighbour node +# possible_node.g = current_node.g + 1 +# possible_node.h = self.manhattan_distance(current_node.location, food_loc) +# possible_node.f = possible_node.g + possible_node.h +# +# # If the node's location is also in the open list but has a worse g value, ignore since we can do better +# for open_node in open_list: +# if possible_node.location == open_node.location and possible_node.g > open_node.g: +# break +# +# # Otherwise, this is the best we can do so far - add to open list +# open_list.append(possible_node) +# +# closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours +# timer += 1 +# +# return None, None +# +# +# class Node: +# def __init__(self, location, parent=None, f=0, g=0, h=0): +# self.location = location +# self.neighbour_nodes = [] +# self.parent = parent +# self.f = f # The cost from the start node to the goal node +# self.g = g # The cost from the start node to the current node +# self.h = h # The cost from the current node to the goal node using a heuristic +# +# def __repr__(self): +# return str(self.location) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 5aad892a..19288ee5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,3 @@ Flask==2.3.2 + +numpy~=1.26.0 \ No newline at end of file From 3cadee8777f03fd5541653d61c60715fc8a02792 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 16 Oct 2023 16:20:41 +0100 Subject: [PATCH 06/64] Changed main.py --- main.py | 128 +++----------------------------------------------------- 1 file changed, 6 insertions(+), 122 deletions(-) diff --git a/main.py b/main.py index 158bb830..2f236d0a 100644 --- a/main.py +++ b/main.py @@ -6,22 +6,11 @@ # |________/(______/__| |__| |____/\_____>______>___|__(______/__|__\\_____> # -import numpy as np -import random -import time import typing -from helper_battlesnake import a_star_search -from helper_battlesnake import fill_search -from helper_battlesnake import flood_fill -from helper_battlesnake import look_ahead -from helper_battlesnake import obvious_moves -from helper_battlesnake import order_food -from helper_battlesnake import snake_compass +from minimax import Battlesnake -global board_log - def info() -> typing.Dict: """ info is called when you create your Battlesnake on play.battlesnake.com @@ -42,8 +31,6 @@ def info() -> typing.Dict: def start(game_state: typing.Dict): """start is called when your Battlesnake begins a game""" print("GAME START") - global board_log - board_log = open("board_log.txt", "w") def end(game_state: typing.Dict): @@ -51,120 +38,17 @@ def end(game_state: typing.Dict): print("GAME OVER\n") -def move(game_state: typing.Dict, log=True, timer_log=True) -> typing.Dict: +def move(game_state: typing.Dict) -> typing.Dict: """ move is called on every turn and returns your next move Valid moves are "up", "down", "left", or "right" See https://docs.battlesnake.com/api/example-move for available data """ - clock_in = time.time_ns() - my_head = game_state["you"]["body"][0] # Coordinates of your head - my_length = game_state["you"]["length"] # Length of your snake - + game = Battlesnake(game_state) # Find the safe moves from current position - safe_moves = obvious_moves(game_state, my_head) - if timer_log: - print(f"Found safe moves in {(time.time_ns() - clock_in) // 1000000} ms") - if log: - print(f"Safe moves: {safe_moves}") - - if len(safe_moves) == 0: - risky_moves = obvious_moves(game_state, my_head, risk_averse=False) - if len(risky_moves) == 1: - return {"move": risky_moves[0]} - elif len(risky_moves) == 0: - print(f"MOVE {game_state['turn']}: No safe moves detected! Moving down") - return {"move": "down"} - else: - safe_moves = risky_moves - - # Move towards food instead of random, to regain health and survive longer - food_dist = [] - food_moves = [] - food_fill = [] - food_heur = [] - # Big idea: loop through the most convenient food and find the shortest path using A* search - sorted_food_list = order_food(game_state, my_head) - if log: - print(f"Narrowed down {len(sorted_food_list)} food options: {sorted_food_list}") - for food_loc in sorted_food_list: - clock_in = time.time_ns() - best_path, best_dist = a_star_search(game_state, my_head, food_loc) - if timer_log: - if best_path is None: - print(f"Failed A* search in {(time.time_ns() - clock_in) // 1000000} ms") - else: - print(f"Ran A* search successfully in {(time.time_ns() - clock_in) // 1000000} ms") - - # Grab the output if A* search ran to completion AND run flood fill just in case - if best_path is not None: - clock_in = time.time_ns() - # Grab the shortest distance to the food - food_dist.append(best_dist) - way_to_food = snake_compass(my_head, best_path[-2]) - # Grab the direction to the food - food_moves.append(way_to_food) - f = flood_fill(game_state, look_ahead(my_head, way_to_food)) - food_fill.append(f) - # Grab the heuristic computed for the food - heuristic = best_dist * 11 * 11 / f ** 2.25 - food_heur.append(heuristic) - - if timer_log: - print(f"Ran flood fill and computed heuristic in {(time.time_ns() - clock_in) // 1000000} ms") - if log: - print(f"Distance: {best_dist}, Fill: {f}, Heuristic: {round(heuristic, 5)}") - - # Kinda annoying, but A* sometimes fails because of time complexity. Condition on whether A* ran or not... - food_risk_too_high = False - if len(food_dist) > 0 and my_length <= 25: # If there's food and we're not chilling - next_fill = min(food_fill) - # Probably going to get trapped if we go here - if next_fill < 25: - food_risk_too_high = True - if log: - print(f"If we eat, the fill is {next_fill} so skip") - - if not food_risk_too_high: - next_move = food_moves[np.argmin(food_heur)] - # This bug shouldn't happen - if next_move not in safe_moves: - raise ValueError - if log: - best_food_loc = sorted_food_list[np.argmin(food_heur)] - print(f"Best A* search is {best_food_loc}") - print(f"Going {next_move} which is {min(food_dist)} away from {my_head}") - print(f"MOVE {game_state['turn']}: {next_move}") - print("=" * 10) - return {"move": next_move} - - best_fill = -1 - next_move = safe_moves[0] - for safe_move in safe_moves: - end_square = look_ahead(my_head, safe_move) - max_fill = flood_fill(game_state, next_square=end_square) - if max_fill > best_fill: - next_move = safe_move - best_fill = max_fill - - - # Optimise based on flood fill - next_move = fill_search(game_state, my_head, safe_moves, log) - - if best_fill < 5: - risky_moves = obvious_moves(game_state, my_head, risk_averse=False) - print(f"Risk-averse may cause death with fill = {best_fill}") - next_move = fill_search(game_state, my_head, risky_moves, log) - print(f"Changing to risky with fill = {best_fill}") - if log: - print(f"No A * but best flood fill: {next_move}") - - print(f"MOVE {game_state['turn']}: {next_move}") - print("=" * 10) - # board_log.write(f"MOVE {game_state['turn']}\n") - # board_log.write(str(game_state)) - # board_log.write("\n" + "=" * 100) - return {"move": next_move} + optimal_move = game.minimax_move() + print(f"MOVE {game_state['turn']}: {optimal_move}") + return {"move": optimal_move} # Start server when `python main.py` is run From 5b2ae597f4c6eb59618a76bca74ab52f655421c3 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 16 Oct 2023 17:37:35 +0100 Subject: [PATCH 07/64] New changes pt 2 --- minimax.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/minimax.py b/minimax.py index c9f95f9f..78ecc5d7 100644 --- a/minimax.py +++ b/minimax.py @@ -312,9 +312,10 @@ def heuristic(self, depth_number): enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 food_weight = 75 depth_weight = 25 - length_weight = 150 + length_weight = 300 centre_control_weight = 10 - aggression_weight = 2500 + aggression_weight = 2500 if dist_to_enemy > 0 else 0 + logging.info(f"Available space: {available_space}") logging.info(f"Distance to nearest enemy: {dist_to_enemy}") logging.info(f"Distance to nearest food: {dist_food}") @@ -322,6 +323,7 @@ def heuristic(self, depth_number): logging.info(f"Available enemy space: {available_enemy_space}") logging.info(f"In centre: {in_centre}") logging.info(f"Length: {self.my_length}") + h = (available_space * space_weight) + \ (food_weight / (dist_food + 1)) + \ (layers_deep * depth_weight) + \ @@ -422,7 +424,7 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): new_game.my_health -= 1 # If food was consumed, this elongates the snake from the tail and restores health - if new_head in self.food: + if new_head in self.food and (evaluate_deaths or snake_id == self.my_id): new_game.all_snakes_dict[snake_id]["length"] += 1 new_game.all_snakes_dict[snake_id]["health"] = 100 new_game.all_snakes_dict[snake_id]["body"] += [new_game.all_snakes_dict[snake_id]["body"][-1]] @@ -542,7 +544,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(self.opponents) == 1: search_within = self.board_width * self.board_height elif len(self.opponents) <= 3: - search_within = self.board_width * 2 + search_within = self.board_width elif len(self.opponents) < 6: search_within = self.board_width // 2 else: From 1342f46378a130446290a4eaa486118b3632ec11 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 16 Oct 2023 20:56:07 +0100 Subject: [PATCH 08/64] New changes pt 3 --- minimax.py | 65 ++++++++++++++++++++++++++++++++---------------------- 1 file changed, 39 insertions(+), 26 deletions(-) diff --git a/minimax.py b/minimax.py index 78ecc5d7..9a946b83 100644 --- a/minimax.py +++ b/minimax.py @@ -35,12 +35,19 @@ def __init__(self, game_state): "length": snake["length"], "health": snake["health"] } + if self.my_id not in self.all_snakes_dict.keys(): + self.all_snakes_dict[self.my_id] = { + "head": self.my_head, + "neck": self.my_neck, + "body": self.my_body, + "length": self.my_length, + "health": self.my_health + } # Opponent snakes self.opponents = self.all_snakes_dict.copy() self.opponents.pop(self.my_id) self.update_board() - # self.board_copy = copy.deepcopy(self.board) self.minimax_search_depth = 4 self.peripheral_dim = 3 @@ -281,19 +288,20 @@ def heuristic(self, depth_number): layers_deep = self.minimax_search_depth - depth_number # If an opponent snake dies :) + opponents_left = len(self.opponents) # Determine available space via flood fill available_space = self.flood_fill(self.my_id, risk_averse=True) - # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in - # our vicinity) - if len(self.opponents) <= 4 \ - and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 4 \ - and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): - self.peripheral_dim = 4 - available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") - else: - available_enemy_space = 0 + # # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in + # # our vicinity) + # if len(self.opponents) <= 4 \ + # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 4 \ + # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + # self.peripheral_dim = 4 + # available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") + # else: + # available_enemy_space = 0 if 2 >= len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): dist_to_enemy = self.dist_from_enemies()[0] @@ -309,28 +317,32 @@ def heuristic(self, depth_number): # Heuristic formula space_weight = 0.5 + enemy_left_weight = 1000 enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 food_weight = 75 depth_weight = 25 length_weight = 300 centre_control_weight = 10 - aggression_weight = 2500 if dist_to_enemy > 0 else 0 + aggression_weight = 250 if dist_to_enemy > 0 else 0 logging.info(f"Available space: {available_space}") + logging.info(f"Enemies left: {opponents_left}") logging.info(f"Distance to nearest enemy: {dist_to_enemy}") logging.info(f"Distance to nearest food: {dist_food}") logging.info(f"Layers deep in search tree: {layers_deep}") - logging.info(f"Available enemy space: {available_enemy_space}") + # logging.info(f"Available enemy space: {available_enemy_space}") logging.info(f"In centre: {in_centre}") logging.info(f"Length: {self.my_length}") h = (available_space * space_weight) + \ + (enemy_left_weight / (opponents_left + 1)) + \ (food_weight / (dist_food + 1)) + \ (layers_deep * depth_weight) + \ (self.my_length * length_weight) + \ in_centre * centre_control_weight + \ - (enemy_restriction_weight / (available_enemy_space + 1)) + \ aggression_weight / (dist_to_enemy + 1) + # (enemy_restriction_weight / (available_enemy_space + 1)) + \ + return h @@ -445,9 +457,10 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) ]) + lengths = overlapping_snakes[:, 1].astype(int) # Special cases where the snake committed suicide and also killed our snake => don't remove - if self.my_id not in overlapping_snakes[:, 0]: - lengths = overlapping_snakes[:, 1].astype(int) + if not (self.my_id in overlapping_snakes[:, 0] + and np.count_nonzero(overlapping_snakes[:, 1] == self.my_length) > 1): indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() if len(indices_largest_snakes) > 1: winner_id = None @@ -682,14 +695,14 @@ def minimax(self, depth, alpha, beta, maximising_snake): # return None, None # # -# class Node: -# def __init__(self, location, parent=None, f=0, g=0, h=0): -# self.location = location -# self.neighbour_nodes = [] -# self.parent = parent -# self.f = f # The cost from the start node to the goal node -# self.g = g # The cost from the start node to the current node -# self.h = h # The cost from the current node to the goal node using a heuristic -# -# def __repr__(self): -# return str(self.location) \ No newline at end of file +class Node: + def __init__(self, location, parent=None, f=0, g=0, h=0): + self.location = location + self.neighbour_nodes = [] + self.parent = parent + self.f = f # The cost from the start node to the goal node + self.g = g # The cost from the start node to the current node + self.h = h # The cost from the current node to the goal node using a heuristic + + def __repr__(self): + return str(self.location) \ No newline at end of file From e235abb41a561c522876dcfa2f02ca82a0650c1e Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 16 Oct 2023 23:31:30 +0100 Subject: [PATCH 09/64] New changes pt 3 --- minimax.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/minimax.py b/minimax.py index 9a946b83..5b63ea2a 100644 --- a/minimax.py +++ b/minimax.py @@ -194,7 +194,7 @@ def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, so possible_moves.discard(move) # If the snake is risk-averse, avoid any chance of head-on collisions only if our snake is shorter elif snake_id != opp_snake_id and length <= opp_snake["length"] \ - and self.manhattan_distance(possible_hit, opp_snake["head"]) == 1: + and self.manhattan_distance(possible_hit, opp_snake["head"]) == 0: risky_moves.append(move) if risk_averse: possible_moves.discard(move) @@ -303,7 +303,7 @@ def heuristic(self, depth_number): # else: # available_enemy_space = 0 - if 2 >= len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): dist_to_enemy = self.dist_from_enemies()[0] else: dist_to_enemy = 0 From 98282172c7c54999d57ea227cb08a8be29527542 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 17 Oct 2023 00:16:26 +0100 Subject: [PATCH 10/64] New changes pt 4 --- minimax.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/minimax.py b/minimax.py index 5b63ea2a..b6450a9f 100644 --- a/minimax.py +++ b/minimax.py @@ -35,6 +35,13 @@ def __init__(self, game_state): "length": snake["length"], "health": snake["health"] } + if "name" in game_state["you"] and game_state["you"]["name"] != "Nightwing" and snake["name"] == "Nightwing": + self.my_id = snake["id"] + self.my_head = snake["head"] + self.my_neck = snake["body"][1] + self.my_body = snake["body"] + self.my_length = snake["length"] + self.my_health = snake["health"] if self.my_id not in self.all_snakes_dict.keys(): self.all_snakes_dict[self.my_id] = { "head": self.my_head, @@ -493,7 +500,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): # Otherwise, if our snake is ALIVE and is the winner :) elif game_over: logging.info("OUR SNAKE WON") - return 1e6, None + return 1e6 + depth, None # Reward faster kills # At the bottom of the decision tree or if we won/lost the game if depth == 0: From 51326ffdca2a7e24e0c1bacd6a25ef0994e26687 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 17 Oct 2023 10:24:48 +0100 Subject: [PATCH 11/64] New changes pt 5 --- minimax.py | 45 +++++++++++++++++++++++++++++---------------- 1 file changed, 29 insertions(+), 16 deletions(-) diff --git a/minimax.py b/minimax.py index b6450a9f..335542c4 100644 --- a/minimax.py +++ b/minimax.py @@ -90,6 +90,7 @@ def __copy__(self): def update_board(self): """Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner""" + self.board = np.full((self.board_width, self.board_height), " ") for num, my_square in enumerate(self.my_body): self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "O" for opponent in self.opponents.values(): @@ -197,12 +198,17 @@ def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, so for move in possible_moves.copy(): # Remaining moves possible_hit = self.look_ahead(head, move) # If the potential move hits a snake's body, it's invalid (exclude the tail since it moves forward) - if possible_hit in opp_snake["body"][:-1] and possible_hit != self.my_head: + if snake_id == self.my_id and possible_hit in opp_snake["body"][:-1]: + possible_moves.discard(move) + elif snake_id != self.my_id and possible_hit != self.my_head and ( + (possible_hit in opp_snake["body"][:-1] and opp_snake_id != self.my_id) + or (possible_hit in opp_snake["body"] and opp_snake_id == self.my_id)): possible_moves.discard(move) # If the snake is risk-averse, avoid any chance of head-on collisions only if our snake is shorter elif snake_id != opp_snake_id and length <= opp_snake["length"] \ - and self.manhattan_distance(possible_hit, opp_snake["head"]) == 0: - risky_moves.append(move) + and self.manhattan_distance(possible_hit, opp_snake["head"]) <= 1: + if not (snake_id != self.my_id and opp_snake_id == self.my_id): # Allow opponents to be suicidal + risky_moves.append(move) if risk_averse: possible_moves.discard(move) @@ -410,7 +416,8 @@ def dist_to_nearest_food(self): best_dist = np.inf for food in self.food: dist = self.manhattan_distance(food, self.my_head) - if dist < best_dist: + dist_enemy = min([self.manhattan_distance(food, snake["head"]) for snake in self.opponents.values()]) + if dist < best_dist and ((dist_enemy >= 4 and dist <= dist_enemy * 2) or (4 > dist_enemy >= dist)): best_dist = dist return best_dist @@ -443,19 +450,25 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): new_game.my_health -= 1 # If food was consumed, this elongates the snake from the tail and restores health - if new_head in self.food and (evaluate_deaths or snake_id == self.my_id): - new_game.all_snakes_dict[snake_id]["length"] += 1 - new_game.all_snakes_dict[snake_id]["health"] = 100 - new_game.all_snakes_dict[snake_id]["body"] += [new_game.all_snakes_dict[snake_id]["body"][-1]] - new_game.food = [food for food in self.food if not (food["x"] == new_head["x"] and food["y"] == new_head["y"])] - # Repeat for our snake specifically - if snake_id == self.my_id: - new_game.my_length += 1 - new_game.my_health = 100 - new_game.my_body += [new_game.my_body[-1]] + if new_head in self.food: + new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head # Check if any snakes died from this simulated move and remove them from the game if evaluate_deaths: + # Update snake lengths from any food eaten + for update_id, snake in new_game.all_snakes_dict.items(): + if "food_eaten" in snake.keys(): + new_game.all_snakes_dict[update_id]["length"] += 1 + new_game.all_snakes_dict[update_id]["health"] = 100 + new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] + new_game.food = [food for food in self.food + if not (food["x"] == snake["food_eaten"]["x"] + and food["y"] == snake["food_eaten"]["y"])] + if update_id == self.my_id: + new_game.my_length += 1 + new_game.my_health = 100 + new_game.my_body += [new_game.my_body[-1]] + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] count_heads = Counter(all_heads) butt_heads = [k for k, v in count_heads.items() if v > 1] @@ -467,7 +480,7 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): lengths = overlapping_snakes[:, 1].astype(int) # Special cases where the snake committed suicide and also killed our snake => don't remove if not (self.my_id in overlapping_snakes[:, 0] - and np.count_nonzero(overlapping_snakes[:, 1] == self.my_length) > 1): + and np.count_nonzero(lengths == self.my_length) > 1): indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() if len(indices_largest_snakes) > 1: winner_id = None @@ -586,7 +599,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(self.opponents) <= 5: opps_moves[opp_id] = [opp_move[0]] - logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {self.board_width // 2} " + logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " f"squares of us in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") clock_in = time.time_ns() From b59e505d51692bb8e2b195a0634d7fe476973f2a Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 17 Oct 2023 11:11:34 +0100 Subject: [PATCH 12/64] New changes pt 6 --- minimax.py | 27 ++++++++++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/minimax.py b/minimax.py index 335542c4..a605c45e 100644 --- a/minimax.py +++ b/minimax.py @@ -132,8 +132,20 @@ def peripheral_vision(self, snake_id, direction): """Return [x1, x2] and [y1, y2] of a portion of the board that functions as 'peripheral vision' for the snake""" # Our peripheral field of vision when scanning for moves head = self.all_snakes_dict[snake_id]["head"].copy() + neck = self.all_snakes_dict[snake_id]["neck"].copy() dim = self.peripheral_dim + if direction == "auto": # Gotta figure out the direction ourselves + if neck["x"] < head["x"]: + direction = "right" + elif neck["x"] > head["x"]: + direction = "left" + elif neck["y"] < head["y"]: + direction = "up" + elif neck["y"] > head["y"]: + direction = "down" + head = neck.copy() # Roll back our head location + if direction == "right": # Neck is left of head, don't move left peripheral_box_x = head["x"] + 1, min(head["x"] + dim + 1, self.board_width) peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) @@ -141,7 +153,7 @@ def peripheral_vision(self, snake_id, direction): elif direction == "left": # Neck is right of head, don't move right peripheral_box_x = max(head["x"] - dim, 0), head["x"] peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) - head["x"], head["y"] = max(head["x"] - peripheral_box_x[0] - 1, 0), head["y"] - peripheral_box_y[0] - 1 + head["x"], head["y"] = max(head["x"] - peripheral_box_x[0] - 1, 0), head["y"] - peripheral_box_y[0] elif direction == "up": # Neck is below head, don't move down peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) peripheral_box_y = head["y"] + 1, min(head["y"] + dim + 1, self.board_height) @@ -150,7 +162,7 @@ def peripheral_vision(self, snake_id, direction): peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) peripheral_box_y = max(head["y"] - dim, 0), head["y"] head["x"], head["y"] = head["x"] - peripheral_box_x[0], max(head["y"] - peripheral_box_y[0] - 1, 0) - else: + else: # Centred around our snake's head peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) head["x"], head["y"] = head["x"] - peripheral_box_x[0], head["y"] - peripheral_box_y[0] @@ -231,7 +243,7 @@ def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, so logging.info(f"Identified obvious moves {list(possible_moves)} in " f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return list(possible_moves) + return possible_moves def minimax_move(self): """Let's run the minimax algorithm with alpha-beta pruning!""" @@ -306,6 +318,9 @@ def heuristic(self, depth_number): # Determine available space via flood fill available_space = self.flood_fill(self.my_id, risk_averse=True) + # Estimate the space we have in our peripheral vision + available_peripheral = self.flood_fill(self.my_id, confined_area="auto") + # # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in # # our vicinity) # if len(self.opponents) <= 4 \ @@ -330,6 +345,7 @@ def heuristic(self, depth_number): # Heuristic formula space_weight = 0.5 + peripheral_weight = 1 enemy_left_weight = 1000 enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 food_weight = 75 @@ -339,6 +355,7 @@ def heuristic(self, depth_number): aggression_weight = 250 if dist_to_enemy > 0 else 0 logging.info(f"Available space: {available_space}") + logging.info(f"Available peripheral: {available_peripheral}") logging.info(f"Enemies left: {opponents_left}") logging.info(f"Distance to nearest enemy: {dist_to_enemy}") logging.info(f"Distance to nearest food: {dist_food}") @@ -348,6 +365,7 @@ def heuristic(self, depth_number): logging.info(f"Length: {self.my_length}") h = (available_space * space_weight) + \ + (peripheral_weight * available_peripheral) + \ (enemy_left_weight / (opponents_left + 1)) + \ (food_weight / (dist_food + 1)) + \ (layers_deep * depth_weight) + \ @@ -532,8 +550,11 @@ def minimax(self, depth, alpha, beta, maximising_snake): clock_in = time.time_ns() possible_moves = self.get_obvious_moves( # If > 6 opponents, we'll do depth = 2 and risk_averse = True self.my_id, risk_averse=(len(self.opponents) > 6), sort_by_peripheral=True) + if len(possible_moves) == 0 and len(self.opponents) > 6: # Try again, but do any risky move + possible_moves = self.get_obvious_moves(self.my_id, risk_averse=False, sort_by_peripheral=True) if len(possible_moves) == 0: # RIP possible_moves = ["down"] + best_val, best_move = -np.inf, None for num, move in enumerate(possible_moves): SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) From 243a79cb3ba018b32bccacb78a811c85077f2c13 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 17 Oct 2023 11:50:14 +0100 Subject: [PATCH 13/64] New changes pt 7 --- minimax.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/minimax.py b/minimax.py index a605c45e..a1760d6f 100644 --- a/minimax.py +++ b/minimax.py @@ -35,6 +35,8 @@ def __init__(self, game_state): "length": snake["length"], "health": snake["health"] } + if "food_eaten" in snake.keys(): + self.all_snakes_dict[snake["id"]]["food_eaten"] = snake["food_eaten"] if "name" in game_state["you"] and game_state["you"]["name"] != "Nightwing" and snake["name"] == "Nightwing": self.my_id = snake["id"] self.my_head = snake["head"] @@ -70,6 +72,8 @@ def __copy__(self): "length": snake["length"], "health": snake["health"] }) + if "food_eaten" in snake.keys(): + all_snakes[-1]["food_eaten"] = snake["food_eaten"] board = { "height": self.board_height, "width": self.board_width, @@ -219,7 +223,7 @@ def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, so # If the snake is risk-averse, avoid any chance of head-on collisions only if our snake is shorter elif snake_id != opp_snake_id and length <= opp_snake["length"] \ and self.manhattan_distance(possible_hit, opp_snake["head"]) <= 1: - if not (snake_id != self.my_id and opp_snake_id == self.my_id): # Allow opponents to be suicidal + if not (snake_id != self.my_id and opp_snake_id == self.my_id and length == opp_snake["length"]): # Allow opponents to be suicidal (so our snakes are the same size) risky_moves.append(move) if risk_averse: possible_moves.discard(move) @@ -487,6 +491,9 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): new_game.my_health = 100 new_game.my_body += [new_game.my_body[-1]] + # Reset the food tracker + del new_game.all_snakes_dict[update_id]["food_eaten"] + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] count_heads = Counter(all_heads) butt_heads = [k for k, v in count_heads.items() if v > 1] From 32f5e1afa14d3701df5a05624965f1223b976fa9 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 17 Oct 2023 12:30:41 +0100 Subject: [PATCH 14/64] New changes pt 8 --- minimax.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/minimax.py b/minimax.py index a1760d6f..4e41292e 100644 --- a/minimax.py +++ b/minimax.py @@ -335,11 +335,17 @@ def heuristic(self, depth_number): # else: # available_enemy_space = 0 + # Get closer to enemy snakes if we're longer by 3 if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): dist_to_enemy = self.dist_from_enemies()[0] else: dist_to_enemy = 0 + # If we're getting too close to enemy snakes that are longer, RETREAT + threats = [self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] + num_threats = (np.count_nonzero(np.array(threats) <= 2) * 2 + + np.count_nonzero(np.array(threats) == 3) * 1) + # Determine the closest safe distance to food dist_food = self.dist_to_nearest_food() @@ -357,10 +363,12 @@ def heuristic(self, depth_number): length_weight = 300 centre_control_weight = 10 aggression_weight = 250 if dist_to_enemy > 0 else 0 + threat_proximity_weight = -100 logging.info(f"Available space: {available_space}") logging.info(f"Available peripheral: {available_peripheral}") logging.info(f"Enemies left: {opponents_left}") + logging.info(f"Threats within 3 squares of us: {num_threats}") logging.info(f"Distance to nearest enemy: {dist_to_enemy}") logging.info(f"Distance to nearest food: {dist_food}") logging.info(f"Layers deep in search tree: {layers_deep}") @@ -371,6 +379,7 @@ def heuristic(self, depth_number): h = (available_space * space_weight) + \ (peripheral_weight * available_peripheral) + \ (enemy_left_weight / (opponents_left + 1)) + \ + (threat_proximity_weight * num_threats) + \ (food_weight / (dist_food + 1)) + \ (layers_deep * depth_weight) + \ (self.my_length * length_weight) + \ From 331d42b81633a7ccf4470800f4e8972928b65114 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 17 Oct 2023 13:46:05 +0100 Subject: [PATCH 15/64] New changes pt 9 --- minimax.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/minimax.py b/minimax.py index 4e41292e..f99e1ca8 100644 --- a/minimax.py +++ b/minimax.py @@ -153,7 +153,7 @@ def peripheral_vision(self, snake_id, direction): if direction == "right": # Neck is left of head, don't move left peripheral_box_x = head["x"] + 1, min(head["x"] + dim + 1, self.board_width) peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) - head["x"], head["y"] = 0, head["y"] - peripheral_box_y[0] - 1 + head["x"], head["y"] = 0, head["y"] - peripheral_box_y[0] elif direction == "left": # Neck is right of head, don't move right peripheral_box_x = max(head["x"] - dim, 0), head["x"] peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) @@ -348,22 +348,25 @@ def heuristic(self, depth_number): # Determine the closest safe distance to food dist_food = self.dist_to_nearest_food() + health_flag = True if self.my_health < 40 else False + shortest_flag = True if sum([self.my_length <= snake["length"] for snake in self.opponents.values()]) >= 2 else False + # Are we in the centre of the board? Maximise control centre = range(self.board_width // 2 - 2, self.board_width // 2 + 3) in_centre = (self.my_head["x"] in centre and self.my_head["x"] in centre) and (len(self.opponents) <= 2) # Heuristic formula - space_weight = 0.5 - peripheral_weight = 1 + space_weight = 1 + peripheral_weight = 2 enemy_left_weight = 1000 enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 - food_weight = 75 + food_weight = 250 if health_flag else 175 if shortest_flag else 75 depth_weight = 25 length_weight = 300 centre_control_weight = 10 aggression_weight = 250 if dist_to_enemy > 0 else 0 - threat_proximity_weight = -100 + threat_proximity_weight = -45 logging.info(f"Available space: {available_space}") logging.info(f"Available peripheral: {available_peripheral}") From d738f0e605b1b33a41feeb878283e6e0fab1bd22 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 15:01:00 +0100 Subject: [PATCH 16/64] Changes pt 10 --- minimax.py | 29 +++++++++++++++++++++++------ 1 file changed, 23 insertions(+), 6 deletions(-) diff --git a/minimax.py b/minimax.py index f99e1ca8..7a2bfb15 100644 --- a/minimax.py +++ b/minimax.py @@ -2,12 +2,15 @@ import itertools import logging import numpy as np +import pandas as pd import sys import time from collections import Counter logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) -# logging.disable(logging.INFO) +logging.disable(logging.INFO) + +# tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} class Battlesnake: @@ -102,7 +105,7 @@ def update_board(self): for num, snake_sq in enumerate(snake_body): self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "X" - def display_board(self, board=None): + def display_board(self, board=None, return_string=False): """Print out a nicely formatted board for convenient debugging""" render_board = board if board is not None else self.board for j in range(1, len(render_board[0]) + 1): @@ -111,6 +114,15 @@ def display_board(self, board=None): display_row += f"{render_board[i][-j]}| " logging.info(display_row) + if return_string: + board_str = "" + for j in range(1, len(render_board[0]) + 1): + display_row = "" + for i in range(0, len(render_board)): + display_row += f"{render_board[i][-j]}| " + board_str += display_row + "\n" + return board_str + @staticmethod def look_ahead(head, move): """Given a possible move, return the snake's new coordinates if it were headed that way""" @@ -349,7 +361,7 @@ def heuristic(self, depth_number): # Determine the closest safe distance to food dist_food = self.dist_to_nearest_food() health_flag = True if self.my_health < 40 else False - shortest_flag = True if sum([self.my_length <= snake["length"] for snake in self.opponents.values()]) >= 2 else False + shortest_flag = True if sum([self.my_length <= snake["length"] for snake in self.opponents.values()]) >= min([2, len(self.opponents)]) else False # Are we in the centre of the board? Maximise control @@ -529,7 +541,7 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): new_game.all_snakes_dict.pop(rm_id) new_game.update_board() - logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") return new_game def minimax(self, depth, alpha, beta, maximising_snake): @@ -581,6 +593,11 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"{len(possible_moves)} CHILD NODES: VISITING {num + 1} OF {len(possible_moves)}") logging.info(f"Running minimax for OUR SNAKE moving {move}") SIMULATED_BOARD_INSTANCE.display_board() + + board_string = SIMULATED_BOARD_INSTANCE.display_board(return_string=True) + # global tree_tracker + # tree_tracker[depth].append(board_string) + clock_in2 = time.time_ns() node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, False) @@ -650,8 +667,8 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") all_opp_combos = all_opp_combos[:2] elif len(all_opp_combos) > 3: - logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 3") - all_opp_combos = all_opp_combos[:3] + logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") + all_opp_combos = all_opp_combos[:2] possible_movesets = [] possible_sims = [] From 1368c06ebf83931b3678fe35ec8effc614034c62 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 15:30:43 +0100 Subject: [PATCH 17/64] Changes pt 11 where we attempt to reduce runtime --- minimax.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/minimax.py b/minimax.py index 7a2bfb15..3e995d06 100644 --- a/minimax.py +++ b/minimax.py @@ -8,7 +8,7 @@ from collections import Counter logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) -logging.disable(logging.INFO) +# logging.disable(logging.INFO) # tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} @@ -544,7 +544,7 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") return new_game - def minimax(self, depth, alpha, beta, maximising_snake): + def minimax(self, depth, alpha, beta, maximising_snake, TEMP_TAG=False): """Implement the minimax algorithm with alpha-beta pruning :param depth: @@ -586,6 +586,9 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(possible_moves) == 0: # RIP possible_moves = ["down"] + if TEMP_TAG: + possible_moves = possible_moves[:2] + best_val, best_move = -np.inf, None for num, move in enumerate(possible_moves): SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) @@ -693,7 +696,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"Running minimax for OPPONENT SNAKES moving {possible_movesets[num]}") SIMULATED_BOARD_INSTANCE.display_board() clock_in2 = time.time_ns() - node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, True) + node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, True, TEMP_TAG=True) logging.info("=" * 50) logging.info(f"BACK AT DEPTH = {depth} OPPONENT SNAKES") From b069eb31ad213b7d5e58329904328d163db4825d Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 15:34:10 +0100 Subject: [PATCH 18/64] Changes pt 11 remove pandas --- minimax.py | 1 - 1 file changed, 1 deletion(-) diff --git a/minimax.py b/minimax.py index 3e995d06..087d60c4 100644 --- a/minimax.py +++ b/minimax.py @@ -2,7 +2,6 @@ import itertools import logging import numpy as np -import pandas as pd import sys import time from collections import Counter From 293ceadf6754d02e944def6948d94a2e4757cd23 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 15:45:41 +0100 Subject: [PATCH 19/64] Changes pt 11 where we attempt to reduce runtime 2 --- minimax.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/minimax.py b/minimax.py index 087d60c4..0fd38b26 100644 --- a/minimax.py +++ b/minimax.py @@ -390,8 +390,8 @@ def heuristic(self, depth_number): logging.info(f"In centre: {in_centre}") logging.info(f"Length: {self.my_length}") - h = (available_space * space_weight) + \ - (peripheral_weight * available_peripheral) + \ + # (available_space * space_weight) + \ + h = (peripheral_weight * available_peripheral) + \ (enemy_left_weight / (opponents_left + 1)) + \ (threat_proximity_weight * num_threats) + \ (food_weight / (dist_food + 1)) + \ From a58c8cbadc954d8450ec8e0993e3335358bf9b5a Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 15:50:09 +0100 Subject: [PATCH 20/64] Changes pt 11 where we attempt to reduce runtime 3 --- minimax.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/minimax.py b/minimax.py index 0fd38b26..897c83c4 100644 --- a/minimax.py +++ b/minimax.py @@ -585,7 +585,7 @@ def minimax(self, depth, alpha, beta, maximising_snake, TEMP_TAG=False): if len(possible_moves) == 0: # RIP possible_moves = ["down"] - if TEMP_TAG: + if TEMP_TAG and len(possible_moves) > 2: possible_moves = possible_moves[:2] best_val, best_move = -np.inf, None @@ -668,7 +668,7 @@ def minimax(self, depth, alpha, beta, maximising_snake, TEMP_TAG=False): if len(all_opp_combos) > 2 and len(self.opponents) > 2: logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") all_opp_combos = all_opp_combos[:2] - elif len(all_opp_combos) > 3: + elif len(all_opp_combos) > 2: logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") all_opp_combos = all_opp_combos[:2] From ce557d184629bc365d804208d11744b7c3f392d2 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 15:54:49 +0100 Subject: [PATCH 21/64] NO PRINT --- minimax.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/minimax.py b/minimax.py index 897c83c4..afa7f554 100644 --- a/minimax.py +++ b/minimax.py @@ -7,7 +7,7 @@ from collections import Counter logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) -# logging.disable(logging.INFO) +logging.disable(logging.INFO) # tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} From d7a6d28da0e556929dff961e6defc7df9e25fd6d Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 16:14:47 +0100 Subject: [PATCH 22/64] PUlled back --- minimax.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/minimax.py b/minimax.py index afa7f554..21ffb6d9 100644 --- a/minimax.py +++ b/minimax.py @@ -585,9 +585,6 @@ def minimax(self, depth, alpha, beta, maximising_snake, TEMP_TAG=False): if len(possible_moves) == 0: # RIP possible_moves = ["down"] - if TEMP_TAG and len(possible_moves) > 2: - possible_moves = possible_moves[:2] - best_val, best_move = -np.inf, None for num, move in enumerate(possible_moves): SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) @@ -596,7 +593,7 @@ def minimax(self, depth, alpha, beta, maximising_snake, TEMP_TAG=False): logging.info(f"Running minimax for OUR SNAKE moving {move}") SIMULATED_BOARD_INSTANCE.display_board() - board_string = SIMULATED_BOARD_INSTANCE.display_board(return_string=True) + # board_string = SIMULATED_BOARD_INSTANCE.display_board(return_string=True) # global tree_tracker # tree_tracker[depth].append(board_string) @@ -668,9 +665,9 @@ def minimax(self, depth, alpha, beta, maximising_snake, TEMP_TAG=False): if len(all_opp_combos) > 2 and len(self.opponents) > 2: logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") all_opp_combos = all_opp_combos[:2] - elif len(all_opp_combos) > 2: + elif len(all_opp_combos) > 3: logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") - all_opp_combos = all_opp_combos[:2] + all_opp_combos = all_opp_combos[:3] possible_movesets = [] possible_sims = [] From 42d49c00d091438c8f9316d50f69b36e8df4b122 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 16:20:49 +0100 Subject: [PATCH 23/64] PUlled back 2 --- minimax.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/minimax.py b/minimax.py index 21ffb6d9..0ab4e40a 100644 --- a/minimax.py +++ b/minimax.py @@ -390,8 +390,8 @@ def heuristic(self, depth_number): logging.info(f"In centre: {in_centre}") logging.info(f"Length: {self.my_length}") - # (available_space * space_weight) + \ - h = (peripheral_weight * available_peripheral) + \ + h = (available_space * space_weight) + \ + (peripheral_weight * available_peripheral) + \ (enemy_left_weight / (opponents_left + 1)) + \ (threat_proximity_weight * num_threats) + \ (food_weight / (dist_food + 1)) + \ From 1538cf0474315984a015f2b375a8943266c7a715 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 18 Oct 2023 16:23:06 +0100 Subject: [PATCH 24/64] Pen --- minimax.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/minimax.py b/minimax.py index 0ab4e40a..dd2713ab 100644 --- a/minimax.py +++ b/minimax.py @@ -332,6 +332,10 @@ def heuristic(self, depth_number): # Determine available space via flood fill available_space = self.flood_fill(self.my_id, risk_averse=True) + if available_space < 4: + space_penalty = -200 + else: + space_penalty = 0 # Estimate the space we have in our peripheral vision available_peripheral = self.flood_fill(self.my_id, confined_area="auto") @@ -390,7 +394,7 @@ def heuristic(self, depth_number): logging.info(f"In centre: {in_centre}") logging.info(f"Length: {self.my_length}") - h = (available_space * space_weight) + \ + h = (available_space * space_weight) + space_penalty + \ (peripheral_weight * available_peripheral) + \ (enemy_left_weight / (opponents_left + 1)) + \ (threat_proximity_weight * num_threats) + \ From ad77d0fa2d11aa27e550ed205f8d2b3659c6f5f3 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 01:40:59 +0100 Subject: [PATCH 25/64] Snake engine --- main.py | 4 +- snake_engine.py | 801 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 802 insertions(+), 3 deletions(-) create mode 100644 snake_engine.py diff --git a/main.py b/main.py index 2f236d0a..c2d24eec 100644 --- a/main.py +++ b/main.py @@ -7,8 +7,7 @@ # import typing - -from minimax import Battlesnake +from snake_engine import Battlesnake def info() -> typing.Dict: @@ -45,7 +44,6 @@ def move(game_state: typing.Dict) -> typing.Dict: See https://docs.battlesnake.com/api/example-move for available data """ game = Battlesnake(game_state) - # Find the safe moves from current position optimal_move = game.minimax_move() print(f"MOVE {game_state['turn']}: {optimal_move}") return {"move": optimal_move} diff --git a/snake_engine.py b/snake_engine.py new file mode 100644 index 00000000..455ad911 --- /dev/null +++ b/snake_engine.py @@ -0,0 +1,801 @@ +import copy +import itertools +import logging +import os +import numpy as np +import sys +import time +from collections import Counter + +logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) +# If we're not running locally, then disable any logging +running_locally = "HDPC6511" in os.popen("hostname").read() +if not running_locally: + logging.disable(logging.INFO) + +# tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} + + +class Battlesnake: + def __init__(self, game_state): + # General game data + self.turn = game_state["turn"] + self.board_width = game_state["board"]["width"] + self.board_height = game_state["board"]["height"] + self.food = game_state["board"]["food"] + self.hazards = game_state["board"]["hazards"] + self.board = np.full((self.board_width, self.board_height), " ") + + # Our snake's data + self.my_id = game_state["you"]["id"] + self.my_head = game_state["you"]["head"] + self.my_neck = game_state["you"]["body"][1] + self.my_body = game_state["you"]["body"] + self.my_length = game_state["you"]["length"] + self.my_health = game_state["you"]["health"] + + # Read snake positions as a dictionary of dictionaries (easier to access than list of dicts) + self.all_snakes_dict = {} + for snake in game_state["board"]["snakes"]: + self.all_snakes_dict[snake["id"]] = { + "head": snake["head"], + "neck": snake["body"][1], + "body": snake["body"], + "length": snake["length"], + "health": snake["health"], + "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else [] + } + # Weird cases when running locally where the "you" snake is not our actual snake or is empty + if ("name" in game_state["you"] and game_state["you"]["name"] != "Nightwing" + and snake["name"] == "Nightwing"): + self.my_id = snake["id"] + self.my_head = snake["head"] + self.my_neck = snake["body"][1] + self.my_body = snake["body"] + self.my_length = snake["length"] + self.my_health = snake["health"] + # Another weird edge case when running locally where our snake is not actually in the "snakes" field + if self.my_id not in self.all_snakes_dict.keys(): + self.all_snakes_dict[self.my_id] = { + "head": self.my_head, + "neck": self.my_neck, + "body": self.my_body, + "length": self.my_length, + "health": self.my_health + } + + # Opponent snakes + self.opponents = self.all_snakes_dict.copy() + self.opponents.pop(self.my_id) + + # Finish up our constructor + self.update_board() + self.minimax_search_depth = 4 + self.peripheral_dim = 3 + + def __copy__(self): + """Making a deep copy of the game_state dictionary takes too much time, so let's manually build it from + scratch. That way, we can modify a copied instance of the class without affecting the original instance.""" + all_snakes = [] + for snake_id, snake in self.all_snakes_dict.items(): + all_snakes.append({ + "id": snake_id, + "head": snake["head"], + "body": snake["body"].copy(), + "length": snake["length"], + "health": snake["health"], + "food_eaten": snake["food_eaten"] + }) + board = { + "height": self.board_height, + "width": self.board_width, + "food": self.food.copy(), + "hazards": self.hazards.copy(), + "snakes": all_snakes + } + you = { + "id": self.my_id, + "health": self.my_health, + "body": self.my_body.copy(), + "head": self.my_head, + "length": self.my_length + } + new_game_state = {"turn": self.turn, "board": board, "you": you} + return Battlesnake(new_game_state) + + def update_board(self): + """Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" + represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner""" + self.board = np.full((self.board_width, self.board_height), " ") + for num, my_square in enumerate(self.my_body): + self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "O" + for opponent in self.opponents.values(): + snake_body = opponent["body"] + for num, snake_sq in enumerate(snake_body): + self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "X" + + def display_board(self, board=None, return_string=False): + """Print out a nicely formatted board for convenient debugging""" + render_board = board if board is not None else self.board + for j in range(1, len(render_board[0]) + 1): + display_row = "" + for i in range(0, len(render_board)): + display_row += f"{render_board[i][-j]}| " + logging.info(display_row) + + # Do you want to return the board as a string instead of printing it out? + if return_string: + board_str = "" + for j in range(1, len(render_board[0]) + 1): + display_row = "" + for i in range(0, len(render_board)): + display_row += f"{render_board[i][-j]}| " + board_str += display_row + "\n" + return board_str + + @staticmethod + def look_ahead(head, move): + """Given a possible move, return the snake's new coordinates if it were headed that way""" + snake_head = head.copy() + if move == "up": + snake_head["y"] += 1 + if move == "down": + snake_head["y"] -= 1 + if move == "left": + snake_head["x"] -= 1 + if move == "right": + snake_head["x"] += 1 + return snake_head + + @staticmethod + def manhattan_distance(start, end): + """Return the Manhattan distance for two positions""" + start = (start["x"], start["y"]) + end = (end["x"], end["y"]) + return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) + + def peripheral_vision(self, snake_id, direction): + """Return [x1, x2] and [y1, y2] of a portion of the board that functions as 'peripheral vision' for the snake""" + # Our peripheral field of vision when scanning for moves + head = self.all_snakes_dict[snake_id]["head"].copy() + neck = self.all_snakes_dict[snake_id]["neck"].copy() + dim = self.peripheral_dim + + if direction == "auto": # Got to figure out the direction ourselves + if neck["x"] < head["x"]: + direction = "right" + elif neck["x"] > head["x"]: + direction = "left" + elif neck["y"] < head["y"]: + direction = "up" + elif neck["y"] > head["y"]: + direction = "down" + head = neck.copy() # Roll back our head location + + if direction == "right": # Neck is left of head, don't move left + peripheral_box_x = head["x"] + 1, min(head["x"] + dim + 1, self.board_width) + peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) + head["x"], head["y"] = 0, head["y"] - peripheral_box_y[0] + elif direction == "left": # Neck is right of head, don't move right + peripheral_box_x = max(head["x"] - dim, 0), head["x"] + peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) + head["x"], head["y"] = max(head["x"] - peripheral_box_x[0] - 1, 0), head["y"] - peripheral_box_y[0] + elif direction == "up": # Neck is below head, don't move down + peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) + peripheral_box_y = head["y"] + 1, min(head["y"] + dim + 1, self.board_height) + head["x"], head["y"] = head["x"] - peripheral_box_x[0], 0 + elif direction == "down": # Neck is above head, don't move up + peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) + peripheral_box_y = max(head["y"] - dim, 0), head["y"] + head["x"], head["y"] = head["x"] - peripheral_box_x[0], max(head["y"] - peripheral_box_y[0] - 1, 0) + else: # Centred around our snake's head + peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) + peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) + head["x"], head["y"] = head["x"] - peripheral_box_x[0], head["y"] - peripheral_box_y[0] + + return peripheral_box_x, peripheral_box_y, head + + def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, sort_by_peripheral=False): + """Return a list of valid moves for any hypothetical snake. If risk_averse is True, avoid any moves that may + potentially cause a collision (but only if the snake is shorter) + + Use cases: + - self.get_obvious_moves(snake_id) will return moves for any opponent snake for the current board + - self.get_obvious_moves(risk_averse=True) will return possible moves that avoid death-inducing collisions with + our snake. Set False to assume our opponents are out to get us (paranoid minimax) + - self.get_obvious_moves(sort_by_dist_to=snake["id"]) will return all possible moves, but sorted by the distance + from our snake post-move to the head of the snake whose ID is given as the parameter input + - self.get_obvious_moves(sort_by_peripheral=True) will return all possible moves, but sorted by the amount of + space that each move is estimated to give us in our "peripheral vision" + """ + risky_moves = [] # Initialise a set of possible risky moves + possible_moves = {"up", "down", "left", "right"} # Initialise a set of possible moves + + head = self.all_snakes_dict[snake_id]["head"] + neck = self.all_snakes_dict[snake_id]["neck"] + length = self.all_snakes_dict[snake_id]["length"] + + clock_in = time.time_ns() + if neck["x"] < head["x"]: # Neck is left of head, don't move left + possible_moves.discard("left") + elif neck["x"] > head["x"]: # Neck is right of head, don't move right + possible_moves.discard("right") + elif neck["y"] < head["y"]: # Neck is below head, don't move down + possible_moves.discard("down") + elif neck["y"] > head["y"]: # Neck is above head, don't move up + possible_moves.discard("up") + + # Prevent snake from moving out of bounds + if head["x"] == 0: + possible_moves.discard("left") + if head["x"] + 1 == self.board_height: + possible_moves.discard("right") + if head["y"] == 0: + possible_moves.discard("down") + if head["y"] + 1 == self.board_width: + possible_moves.discard("up") + + # Prevent snake from colliding with other snakes + for opp_snake_id, opp_snake in self.all_snakes_dict.items(): + for move in possible_moves.copy(): # Remaining moves + possible_hit = self.look_ahead(head, move) + # If the potential move hits a snake's body, it's invalid (exclude the tail since it moves forward) + if snake_id == self.my_id and possible_hit in opp_snake["body"][:-1]: + possible_moves.discard(move) + elif snake_id != self.my_id and possible_hit != self.my_head and ( + (possible_hit in opp_snake["body"][:-1] and opp_snake_id != self.my_id) + or (possible_hit in opp_snake["body"] and opp_snake_id == self.my_id)): + possible_moves.discard(move) + # If the snake is risk-averse, avoid any chance of head-on collisions only if our snake is shorter + elif snake_id != opp_snake_id and length <= opp_snake["length"] \ + and self.manhattan_distance(possible_hit, opp_snake["head"]) <= 1: + # Allow opponents to be suicidal (so our snakes are the same size) + if not (snake_id != self.my_id and opp_snake_id == self.my_id and length == opp_snake["length"]): + risky_moves.append(move) + if risk_averse: + possible_moves.discard(move) + + if sort_by_dist_to is not None: + head2 = self.all_snakes_dict[sort_by_dist_to]["head"] + possible_moves = sorted(possible_moves, # This converts a set to a list anyway + key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) + elif sort_by_peripheral: + possible_moves = sorted(possible_moves, # This converts a set to a list anyway + key=lambda move2: self.flood_fill(snake_id, confined_area=move2), + reverse=True) + else: + possible_moves = list(possible_moves) + + # De-prioritise any risky moves and send them to the back + if len(risky_moves) > 0: + for risky in risky_moves: + if risky in possible_moves: + possible_moves.append(possible_moves.pop(possible_moves.index(risky))) + + logging.info(f"Identified obvious moves {list(possible_moves)} in " + f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") + return possible_moves + + def minimax_move(self): + """Let's run the minimax algorithm with alpha-beta pruning!""" + # Compute the best score of each move using the minimax algorithm with alpha-beta pruning + if self.turn <= 4: # Our first 4 moves are super self-explanatory tbh + search_depth = 1 + elif len(self.opponents) > 6: + search_depth = 2 # TODO should be risk-averse + elif len(self.opponents) >= 4: + search_depth = 4 + else: + search_depth = self.minimax_search_depth + + _, best_move = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) + return best_move + + def is_game_over(self, for_snake_id=None, depth=None): + """Return True if the game ended for our snake, False otherwise. Use for_snake_id to return a boolean for a + specific snake's status (can be one or multiple).""" + snake_monitor = {} # A dictionary for each snake showing whether they're alive + for snake_id, snake in self.all_snakes_dict.items(): + snake_monitor[snake_id] = True + + head = snake["head"] + # Prevent snake from moving out of bounds + if head["x"] < 0 or head["x"] >= self.board_height: + snake_monitor[snake_id] = False + if head["y"] < 0 or head["y"] >= self.board_width: + snake_monitor[snake_id] = False + + # Skip this if we're at the beginning of the game when all snakes are still coiled up + if not (snake["length"] == 3 and snake["body"][-2] == snake["body"][-1]): + # Prevent snake from colliding with any other snakes + for opp_snake_id, opp_snake in self.all_snakes_dict.items(): + # Depending on the depth, exclude the tail if the turn technically isn't over (all players need to + # have made a move) + check_body = opp_snake["body"][1:-1] if depth % 2 == 1 else opp_snake["body"][1:] + + # If the potential move hits a snake's body, it's invalid + if head in check_body: + snake_monitor[snake_id] = False + # If the potential move hits a snake's head, it's invalid only if our snake is shorter + if snake_id != opp_snake_id: + if head == opp_snake["head"] and snake["length"] <= opp_snake["length"]: + snake_monitor[snake_id] = False + + # Game is over if there's only one snake remaining or if our snake died + game_over = True if (sum(snake_monitor.values()) == 1 or not snake_monitor[self.my_id]) else False + + # See if a specific snake is alive or not + if isinstance(for_snake_id, (list, tuple)): + snake_still_alive = [snake_monitor[snake_id] for snake_id in for_snake_id] + else: + snake_still_alive = snake_monitor[self.my_id] if for_snake_id is None else snake_monitor[for_snake_id] + + return game_over, snake_still_alive + + def heuristic(self, depth_number): + """Let's figure out a way to evaluate the current board for our snake :) + + Should be a function of: + - Maximising available space for our snake (flood fill) + - Minimising available space for enemy snakes + - Distance to food + - How far down our game tree we went in our minimax search + """ + # Determine how many layers deep in the game tree we are + layers_deep = self.minimax_search_depth - depth_number + + # If an opponent snake dies :) + opponents_left = len(self.opponents) + + # Determine available space via flood fill + available_space = self.flood_fill(self.my_id, risk_averse=True) + if available_space < 4: + space_penalty = -200 + else: + space_penalty = 0 + + # Estimate the space we have in our peripheral vision + available_peripheral = self.flood_fill(self.my_id, confined_area="auto") + + # # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in + # # our vicinity) + # if len(self.opponents) <= 4 \ + # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 4 \ + # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + # self.peripheral_dim = 4 + # available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") + # else: + # available_enemy_space = 0 + + # Get closer to enemy snakes if we're longer by 3 + if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): + dist_to_enemy = self.dist_from_enemies()[0] + else: + dist_to_enemy = 0 + + # If we're getting too close to enemy snakes that are longer, RETREAT + threats = [self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] + num_threats = (np.count_nonzero(np.array(threats) <= 2) * 2 + + np.count_nonzero(np.array(threats) == 3) * 1) + + # Determine the closest safe distance to food + dist_food = self.dist_to_nearest_food() + health_flag = True if self.my_health < 40 else False + shortest_flag = True if sum([self.my_length <= snake["length"] for snake in self.opponents.values()]) >= min([2, len(self.opponents)]) else False + + + # Are we in the centre of the board? Maximise control + centre = range(self.board_width // 2 - 2, self.board_width // 2 + 3) + in_centre = (self.my_head["x"] in centre and self.my_head["x"] in centre) and (len(self.opponents) <= 2) + + # Heuristic formula + space_weight = 1 + peripheral_weight = 2 + enemy_left_weight = 1000 + enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 + food_weight = 250 if health_flag else 175 if shortest_flag else 75 + depth_weight = 25 + length_weight = 300 + centre_control_weight = 10 + aggression_weight = 250 if dist_to_enemy > 0 else 0 + threat_proximity_weight = -45 + + logging.info(f"Available space: {available_space}") + logging.info(f"Available peripheral: {available_peripheral}") + logging.info(f"Enemies left: {opponents_left}") + logging.info(f"Threats within 3 squares of us: {num_threats}") + logging.info(f"Distance to nearest enemy: {dist_to_enemy}") + logging.info(f"Distance to nearest food: {dist_food}") + logging.info(f"Layers deep in search tree: {layers_deep}") + # logging.info(f"Available enemy space: {available_enemy_space}") + logging.info(f"In centre: {in_centre}") + logging.info(f"Length: {self.my_length}") + + h = (available_space * space_weight) + space_penalty + \ + (peripheral_weight * available_peripheral) + \ + (enemy_left_weight / (opponents_left + 1)) + \ + (threat_proximity_weight * num_threats) + \ + (food_weight / (dist_food + 1)) + \ + (layers_deep * depth_weight) + \ + (self.my_length * length_weight) + \ + in_centre * centre_control_weight + \ + aggression_weight / (dist_to_enemy + 1) + # (enemy_restriction_weight / (available_enemy_space + 1)) + \ + + return h + + def flood_fill(self, snake_id, confined_area=None, risk_averse=False): + """Recursive function to get the total area of the current fill selection. Basically, count how many £ symbols + we can fill while avoiding any $, O, and X symbols (obstacles). Confined_area tells the function to do flood + fill only on one side of the snake (should be either left/right/up/down).""" + head = self.all_snakes_dict[snake_id]["head"] + + # Assume we're doing flood fill for our snake + if snake_id == self.my_id: + board = copy.deepcopy(self.board) + # Let's try to avoid any squares that our enemy can go to + if risk_averse: + threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] + for threat in threats: + x, y = threat["x"], threat["y"] + avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in avoid_sq: + if not (n[0] == head["x"] and n[1] == head["y"]) and \ + (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): + board[n[0]][n[1]] = "X" + # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) + else: + board = np.full((self.board_width, self.board_height), " ") + for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): + board[square["x"], square["y"]] = "£" if num == 0 else "O" + for other_id, other_snake in self.all_snakes_dict.items(): + if other_id != snake_id: + for num, other_square in enumerate(other_snake["body"]): + board[other_square["x"], other_square["y"]] = "$" if num == 0 else "X" + + if confined_area is not None: + xs, ys, head = self.peripheral_vision(snake_id, confined_area) + board = board[xs[0]:xs[1], ys[0]:ys[1]] + # if -1 == head["x"]: + # self.peripheral_vision(snake_id, confined_area) + + def fill(x, y, board, initial_square): + if board[x][y] in ["X", "$"]: # Snakes or hazards + return + if board[x][y] in ["O"]: # Us + return + if board[x][y] == "£" and not initial_square: # Already filled + return + board[x][y] = "£" + neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in neighbours: + if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): + fill(n[0], n[1], board, initial_square=False) + + fill(head["x"], head["y"], board, initial_square=True) + filled = sum((row == "£").sum() for row in board) + return filled - 1 if filled > 0 else filled # Exclude the head, but cannot ever be negative + + def dist_to_nearest_food(self): + """Return the Manhattan distance to the nearest food for our snake""" + best_dist = np.inf + for food in self.food: + dist = self.manhattan_distance(food, self.my_head) + dist_enemy = min([self.manhattan_distance(food, snake["head"]) for snake in self.opponents.values()]) + if dist < best_dist and ((dist_enemy >= 4 and dist <= dist_enemy * 2) or (4 > dist_enemy >= dist)): + best_dist = dist + return best_dist + + def dist_from_enemies(self): + """To gauge how in danger we are, return a list of Manhattan distances to all snakes""" + return sorted([self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values()]) + + def closest_enemy(self): + """Return the nearest enemy snake""" + return sorted(self.opponents.keys(), + key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]))[0] + + def simulate_move(self, move, snake_id, evaluate_deaths=False): + """Create a new Battlesnake instance with a simulated move for a given snake""" + # clock_in = time.time_ns() + new_game = self.__copy__() + + # Simulate the new head position + old_head = self.all_snakes_dict[snake_id]["head"] + new_head = self.look_ahead(old_head, move) + + # Insert the simulated snake position into the new instance + new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] + new_game.all_snakes_dict[snake_id]["head"] = new_head + new_game.all_snakes_dict[snake_id]["health"] -= 1 + # Repeat for our snake's specific attributes + if snake_id == self.my_id: + new_game.my_body = [new_head] + new_game.my_body[:-1] + new_game.my_head = new_head + new_game.my_health -= 1 + + # Keep track if food was consumed - this elongates the snake from the tail and restores health + if new_head in self.food: + new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head + + # Check if any snakes died from this simulated move and remove them from the game + if evaluate_deaths: + # Update snake lengths from any food eaten + for update_id, snake in new_game.all_snakes_dict.items(): + if len(snake["food_eaten"]) > 0: + new_game.all_snakes_dict[update_id]["length"] += 1 + new_game.all_snakes_dict[update_id]["health"] = 100 + new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] + new_game.food = [food for food in self.food # Remove the food from the board + if not (food["x"] == snake["food_eaten"]["x"] + and food["y"] == snake["food_eaten"]["y"])] + if update_id == self.my_id: + new_game.my_length += 1 + new_game.my_health = 100 + new_game.my_body += [new_game.my_body[-1]] + # Reset the food tracker + new_game.all_snakes_dict[update_id]["food_eaten"] = [] + + # Did any snakes die from collisions? + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] + count_heads = Counter(all_heads) + butt_heads = [k for k, v in count_heads.items() if v > 1] + for butt_head in butt_heads: + overlapping_snakes = np.array([ + (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() + if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) + ]) + lengths = overlapping_snakes[:, 1].astype(int) + # Special cases where the snake committed suicide and also killed our snake => don't remove + if not (self.my_id in overlapping_snakes[:, 0] + and np.count_nonzero(lengths == self.my_length) > 1): + indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() + if len(indices_largest_snakes) > 1: + winner_id = None + else: + winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] + + for rm_id in overlapping_snakes[:, 0]: + if rm_id != winner_id: + new_game.all_snakes_dict.pop(rm_id) + + new_game.update_board() + # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + return new_game + + def minimax(self, depth, alpha, beta, maximising_snake): + """Implement the minimax algorithm with alpha-beta pruning + + :param depth: + :param alpha: + :param beta: + :param maximising_snake: + :return: + """ + if depth != self.minimax_search_depth: + # Check if our snake died + game_over, still_alive = self.is_game_over(for_snake_id=self.my_id, depth=depth) + if not still_alive: + logging.info("Our snake died...") + return -1e6 + (self.minimax_search_depth - depth), None # Reward slower deaths + # Otherwise, if our snake is ALIVE and is the winner :) + elif game_over: + logging.info("OUR SNAKE WON") + return 1e6 + depth, None # Reward faster kills + + # At the bottom of the decision tree or if we won/lost the game + if depth == 0: + logging.info("=" * 50) + logging.info(f"DEPTH = {depth}") + heuristic = self.heuristic(depth_number=depth) + logging.info(f"Heuristic = {heuristic} at terminal node") + return heuristic, None + + # Our snake's turn + if maximising_snake: + logging.info("=" * 50) + logging.info(f"DEPTH = {depth} OUR SNAKE") + logging.info(f"ALPHA = {alpha} | beta = {beta}") + + clock_in = time.time_ns() + possible_moves = self.get_obvious_moves( # If > 6 opponents, we'll do depth = 2 and risk_averse = True + self.my_id, risk_averse=(len(self.opponents) > 6), sort_by_peripheral=True) + if len(possible_moves) == 0 and len(self.opponents) > 6: # Try again, but do any risky move + possible_moves = self.get_obvious_moves(self.my_id, risk_averse=False, sort_by_peripheral=True) + if len(possible_moves) == 0: # RIP + possible_moves = ["down"] + + best_val, best_move = -np.inf, None + for num, move in enumerate(possible_moves): + SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) + + logging.info(f"{len(possible_moves)} CHILD NODES: VISITING {num + 1} OF {len(possible_moves)}") + logging.info(f"Running minimax for OUR SNAKE moving {move}") + if running_locally: + SIMULATED_BOARD_INSTANCE.display_board() + + clock_in2 = time.time_ns() + node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, False) + + logging.info("=" * 50) + logging.info(f"BACK AT DEPTH = {depth} OUR SNAKE") + logging.info(f"ALPHA = {alpha} | beta = {beta}") + + # Update best score and best move + if np.argmax([best_val, node_val]) == 1: + best_move = move + best_val = max(best_val, node_val) + old_alpha = alpha + alpha = max(alpha, best_val) + + logging.info(f"Updated ALPHA from {old_alpha} to {alpha}") + logging.info(f"Identified best move so far = {best_move} in {round((time.time_ns() - clock_in2) / 1000000, 3)} ms") + + # Check to see if we can prune + if alpha >= beta: + logging.info(f"PRUNED!!! Alpha = {alpha} >= Beta = {beta}") + break + + logging.info(f"FINISHED MINIMAX LAYER on our snake in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + return best_val, best_move + + # Opponents' turns + else: + logging.info("=" * 50) + logging.info(f"DEPTH = {depth} OPPONENT SNAKES") + logging.info(f"BETA = {beta} | alpha = {alpha}") + + clock_in = time.time_ns() + # If there are multiple opponent snakes, search only for those within a reasonable distance of ours + if len(self.opponents) == 1: + search_within = self.board_width * self.board_height + elif len(self.opponents) <= 3: + search_within = self.board_width + elif len(self.opponents) < 6: + search_within = self.board_width // 2 + else: + search_within = self.board_width // 2 - 1 + + # Grab possible moves for all opponent snakes + opps_nearby = 0 # Counter for opponents in our vicinity + opps_moves = {} # Store possible moves for each snake id + for opp_id, opp_snake in self.opponents.items(): + opp_move = self.get_obvious_moves(opp_id, risk_averse=False, sort_by_dist_to=self.my_id) + if len(opp_move) == 0: # If the snake has no legal moves, move down and die + opp_move = ["down"] + + # Save time by only searching for snakes within close range + if self.manhattan_distance(self.my_head, opp_snake["head"]) < search_within: + opps_moves[opp_id] = opp_move # ALL OF THEM + opps_nearby += 1 + else: + if len(self.opponents) <= 5: + opps_moves[opp_id] = [opp_move[0]] + + logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " + f"squares of us in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + + clock_in = time.time_ns() + # If >= 3 board simulations, then randomly sample 3 of them based on how threatening the position is to our + # snake to cut down on runtime + all_opp_combos = list(itertools.product(*opps_moves.values())) + if len(all_opp_combos) > 2 and len(self.opponents) > 2: + logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") + all_opp_combos = all_opp_combos[:2] + elif len(all_opp_combos) > 3: + logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") + all_opp_combos = all_opp_combos[:3] + + possible_movesets = [] + possible_sims = [] + # Get all possible boards by simulating moves for each opponent snake, one at a time + for move_combo in all_opp_combos: + SIMULATED_BOARD_INSTANCE = self.__copy__() + for num, move in enumerate(move_combo): + evaluate_flag = (num + 1 == len(move_combo)) + SIMULATED_BOARD_INSTANCE = SIMULATED_BOARD_INSTANCE.simulate_move( + move, list(opps_moves.keys())[num], evaluate_deaths=evaluate_flag) + possible_sims.append(SIMULATED_BOARD_INSTANCE.__copy__()) + possible_movesets.append(move_combo) + + logging.info(f"SIMULATED {len(possible_sims)} POSSIBLE BOARDS OF OPPONENT MOVE COMBOS in " + f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") + + clock_in = time.time_ns() + best_val, best_move = np.inf, None + for num, SIMULATED_BOARD_INSTANCE in enumerate(possible_sims): + logging.info(f"{len(possible_sims)} CHILD NODES: VISITING {num + 1} OF {len(possible_sims)}") + logging.info(f"Running minimax for OPPONENT SNAKES moving {possible_movesets[num]}") + if running_locally: + SIMULATED_BOARD_INSTANCE.display_board() + clock_in2 = time.time_ns() + node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, True) + + logging.info("=" * 50) + logging.info(f"BACK AT DEPTH = {depth} OPPONENT SNAKES") + logging.info(f"BETA = {beta} | alpha = {alpha}") + + # Update best score and best move + if np.argmin([best_val, node_val]) == 1: + best_move = possible_movesets[num] + best_val = min(best_val, node_val) + old_beta = beta + beta = min(beta, best_val) + + logging.info(f"Updated BETA from {old_beta} to {beta}") + logging.info(f"Identified best move so far = {best_move} in {round((time.time_ns() - clock_in2) / 1000000, 3)} ms") + + # Check to see if we can prune + if beta <= alpha: + logging.info(f"PRUNED!!! Beta = {beta} <= Alpha = {alpha}") + break + + logging.info(f"FINISHED MINIMAX LAYER on opponents in {(time.time_ns() - clock_in) // 1000000} ms") + return best_val, best_move + +# def a_star_search(self, food_loc): +# """ +# Implement the A* search algorithm to find the shortest path to food +# """ +# head_loc = self.my_head.copy() +# open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet +# closed_list = [] # Contains the nodes which are examined +# timer = 0 +# +# while len(open_list) > 0: +# if timer > 300: +# return None, None +# +# current_node = min(open_list, key=lambda node: node.f) # The node with the smallest f value +# open_list.remove(current_node) # Remove from open list +# +# if current_node.location == food_loc: # Found the food! Stop search +# traceback = [food_loc] +# pointer = current_node +# while pointer.parent is not None: +# traceback.append(pointer.parent.location) +# pointer = pointer.parent +# return traceback, current_node.g +# +# # Identify all possible nodes to move to +# neighbour_nodes = [] +# possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) +# +# for possible_move in possible_moves: +# possible_node = Node(self.look_ahead(current_node.location, possible_move), parent=current_node) +# neighbour_nodes.append(possible_node) +# +# for possible_node in neighbour_nodes: # For each possible new node +# if possible_node.location in closed_list: # Skip if the node is in the closed list +# continue +# +# # Compute f value for the neighbour node +# possible_node.g = current_node.g + 1 +# possible_node.h = self.manhattan_distance(current_node.location, food_loc) +# possible_node.f = possible_node.g + possible_node.h +# +# # If the node's location is also in the open list but has a worse g value, ignore since we can do better +# for open_node in open_list: +# if possible_node.location == open_node.location and possible_node.g > open_node.g: +# break +# +# # Otherwise, this is the best we can do so far - add to open list +# open_list.append(possible_node) +# +# closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours +# timer += 1 +# +# return None, None +# +# +class Node: + def __init__(self, location, parent=None, f=0, g=0, h=0): + self.location = location + self.neighbour_nodes = [] + self.parent = parent + self.f = f # The cost from the start node to the goal node + self.g = g # The cost from the start node to the current node + self.h = h # The cost from the current node to the goal node using a heuristic + + def __repr__(self): + return str(self.location) \ No newline at end of file From a5ac83790c6fce9df1d0a52102b6484616741804 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 01:58:28 +0100 Subject: [PATCH 26/64] Trying out mult --- snake_engine.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 455ad911..a58eec6a 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -27,7 +27,8 @@ def __init__(self, game_state): self.board = np.full((self.board_width, self.board_height), " ") # Our snake's data - self.my_id = game_state["you"]["id"] + self.my_id = game_state["you"]["id"] + "£££" + game_state["you"]["name"] + self.my_name = game_state["you"]["name"] self.my_head = game_state["you"]["head"] self.my_neck = game_state["you"]["body"][1] self.my_body = game_state["you"]["body"] @@ -37,7 +38,7 @@ def __init__(self, game_state): # Read snake positions as a dictionary of dictionaries (easier to access than list of dicts) self.all_snakes_dict = {} for snake in game_state["board"]["snakes"]: - self.all_snakes_dict[snake["id"]] = { + self.all_snakes_dict[snake["id"] + "£££" + snake["name"]] = { "head": snake["head"], "neck": snake["body"][1], "body": snake["body"], @@ -48,7 +49,7 @@ def __init__(self, game_state): # Weird cases when running locally where the "you" snake is not our actual snake or is empty if ("name" in game_state["you"] and game_state["you"]["name"] != "Nightwing" and snake["name"] == "Nightwing"): - self.my_id = snake["id"] + self.my_id = snake["id"] + "£££" + snake["name"] self.my_head = snake["head"] self.my_neck = snake["body"][1] self.my_body = snake["body"] @@ -79,7 +80,8 @@ def __copy__(self): all_snakes = [] for snake_id, snake in self.all_snakes_dict.items(): all_snakes.append({ - "id": snake_id, + "id": snake_id.split("£££")[0], + "name": snake_id.split("£££")[1], "head": snake["head"], "body": snake["body"].copy(), "length": snake["length"], @@ -95,6 +97,7 @@ def __copy__(self): } you = { "id": self.my_id, + "name": self.my_name, "health": self.my_health, "body": self.my_body.copy(), "head": self.my_head, From b1d2ab2eca4eaf0f4466f4838d9310985794d7fb Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 02:11:23 +0100 Subject: [PATCH 27/64] Reverted lol --- snake_engine.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index a58eec6a..455ad911 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -27,8 +27,7 @@ def __init__(self, game_state): self.board = np.full((self.board_width, self.board_height), " ") # Our snake's data - self.my_id = game_state["you"]["id"] + "£££" + game_state["you"]["name"] - self.my_name = game_state["you"]["name"] + self.my_id = game_state["you"]["id"] self.my_head = game_state["you"]["head"] self.my_neck = game_state["you"]["body"][1] self.my_body = game_state["you"]["body"] @@ -38,7 +37,7 @@ def __init__(self, game_state): # Read snake positions as a dictionary of dictionaries (easier to access than list of dicts) self.all_snakes_dict = {} for snake in game_state["board"]["snakes"]: - self.all_snakes_dict[snake["id"] + "£££" + snake["name"]] = { + self.all_snakes_dict[snake["id"]] = { "head": snake["head"], "neck": snake["body"][1], "body": snake["body"], @@ -49,7 +48,7 @@ def __init__(self, game_state): # Weird cases when running locally where the "you" snake is not our actual snake or is empty if ("name" in game_state["you"] and game_state["you"]["name"] != "Nightwing" and snake["name"] == "Nightwing"): - self.my_id = snake["id"] + "£££" + snake["name"] + self.my_id = snake["id"] self.my_head = snake["head"] self.my_neck = snake["body"][1] self.my_body = snake["body"] @@ -80,8 +79,7 @@ def __copy__(self): all_snakes = [] for snake_id, snake in self.all_snakes_dict.items(): all_snakes.append({ - "id": snake_id.split("£££")[0], - "name": snake_id.split("£££")[1], + "id": snake_id, "head": snake["head"], "body": snake["body"].copy(), "length": snake["length"], @@ -97,7 +95,6 @@ def __copy__(self): } you = { "id": self.my_id, - "name": self.my_name, "health": self.my_health, "body": self.my_body.copy(), "head": self.my_head, From 7529f0ff969f615da9e818e134563372e085c488 Mon Sep 17 00:00:00 2001 From: Giabao Tonthat Date: Thu, 19 Oct 2023 02:24:46 +0100 Subject: [PATCH 28/64] Networkx --- networkx_tree.py | 160 +++++++++++++++++++++ test.py | 97 ------------- test_flood_fill.py => visualise_minimax.py | 0 3 files changed, 160 insertions(+), 97 deletions(-) create mode 100644 networkx_tree.py delete mode 100644 test.py rename test_flood_fill.py => visualise_minimax.py (100%) diff --git a/networkx_tree.py b/networkx_tree.py new file mode 100644 index 00000000..4766c3ba --- /dev/null +++ b/networkx_tree.py @@ -0,0 +1,160 @@ +import matplotlib.pyplot as plt +import matplotlib.image as mpimg +import networkx as nx +import random + + +def hierarchy_pos(G, root=None, width=25., vert_gap=0.1, vert_loc=0, leaf_vs_root_factor=1): + ''' + If the graph is a tree this will return the positions to plot this in a + hierarchical layout. + + Based on Joel's answer at https://stackoverflow.com/a/29597209/2966723, + but with some modifications. + + We include this because it may be useful for plotting transmission trees, + and there is currently no networkx equivalent (though it may be coming soon). + + There are two basic approaches we think of to allocate the horizontal + location of a node. + + - Top down: we allocate horizontal space to a node. Then its ``k`` + descendants split up that horizontal space equally. This tends to result + in overlapping nodes when some have many descendants. + - Bottom up: we allocate horizontal space to each leaf node. A node at a + higher level gets the entire space allocated to its descendant leaves. + Based on this, leaf nodes at higher levels get the same space as leaf + nodes very deep in the tree. + + We use use both of these approaches simultaneously with ``leaf_vs_root_factor`` + determining how much of the horizontal space is based on the bottom up + or top down approaches. ``0`` gives pure bottom up, while 1 gives pure top + down. + + + :Arguments: + + **G** the graph (must be a tree) + + **root** the root node of the tree + - if the tree is directed and this is not given, the root will be found and used + - if the tree is directed and this is given, then the positions will be + just for the descendants of this node. + - if the tree is undirected and not given, then a random choice will be used. + + **width** horizontal space allocated for this branch - avoids overlap with other branches + + **vert_gap** gap between levels of hierarchy + + **vert_loc** vertical location of root + + **leaf_vs_root_factor** + + xcenter: horizontal location of root + ''' + if not nx.is_tree(G): + raise TypeError('cannot use hierarchy_pos on a graph that is not a tree') + + if root is None: + if isinstance(G, nx.DiGraph): + root = next(iter(nx.topological_sort(G))) # allows back compatibility with nx version 1.11 + else: + root = random.choice(list(G.nodes)) + + def _hierarchy_pos(G, root, leftmost, width, leafdx=2, vert_gap=0.2, vert_loc=0, + xcenter=0.5, rootpos=None, + leafpos=None, parent=None): + ''' + see hierarchy_pos docstring for most arguments + + pos: a dict saying where all nodes go if they have been assigned + parent: parent of this branch. - only affects it if non-directed + + ''' + + if rootpos is None: + rootpos = {root: (xcenter, vert_loc)} + else: + rootpos[root] = (xcenter, vert_loc) + if leafpos is None: + leafpos = {} + children = list(G.neighbors(root)) + leaf_count = 0 + if not isinstance(G, nx.DiGraph) and parent is not None: + children.remove(parent) + if len(children) != 0: + node_size = width / 50 + + rootdx = width / len(children) + nextx = xcenter - width / 2 - rootdx / 2 + for child in children: + nextx += rootdx + rootpos, leafpos, newleaves = _hierarchy_pos(G, child, leftmost + leaf_count * leafdx, + width=rootdx, leafdx=leafdx, + vert_gap=vert_gap, vert_loc=vert_loc - vert_gap, + xcenter=nextx, rootpos=rootpos, leafpos=leafpos, + parent=root) + leaf_count += newleaves + + leftmostchild = min((x for x, y in [leafpos[child] for child in children])) + rightmostchild = max((x for x, y in [leafpos[child] for child in children])) + leafpos[root] = ((leftmostchild + rightmostchild) / 2, vert_loc) + else: + leaf_count = 1 + leafpos[root] = (leftmost, vert_loc) + # pos[root] = (leftmost + (leaf_count-1)*dx/2., vert_loc) + # print(leaf_count) + return rootpos, leafpos, leaf_count + + xcenter = width / 2. + if isinstance(G, nx.DiGraph): + leafcount = len([node for node in nx.descendants(G, root) if G.out_degree(node) == 0]) + elif isinstance(G, nx.Graph): + leafcount = len([node for node in nx.node_connected_component(G, root) if G.degree(node) == 1 and node != root]) + rootpos, leafpos, leaf_count = _hierarchy_pos(G, root, 0, width, + leafdx=width * 1. / leafcount, + vert_gap=vert_gap, + vert_loc=vert_loc, + xcenter=xcenter) + pos = {} + for node in rootpos: + pos[node] = ( + leaf_vs_root_factor * leafpos[node][0] + (1 - leaf_vs_root_factor) * rootpos[node][0], leafpos[node][1]) + # pos = {node:(leaf_vs_root_factor*x1+(1-leaf_vs_root_factor)*x2, y1) for ((x1,y1), (x2,y2)) in (leafpos[node], rootpos[node]) for node in rootpos} + xmax = max(x for x, y in pos.values()) + for node in pos: + pos[node] = (pos[node][0] * width / xmax, pos[node][1]) + return pos + + +# G = nx.Graph() +# img=mpimg.imread("testgrid.png") +# G.add_node(4,image=img) +# G.add_node("1",image=img) +# G.add_node(2,image=img) +# G.add_node(3,image=img) +# fig=plt.figure(figsize=(12,3)) +# ax=plt.subplot(111) +# G.add_edges_from([("1",2), ("1",3), ("1",4)]) +# pos = hierarchy_pos(G, "1") +# +# nx.draw(G, pos=pos, node_color=["white"] * G.number_of_nodes(), with_labels=True) +# # +# # x,y=pos[5] +# # print(x, y) +# # plt.text(x,y+0.05,s='some text', bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center') +# +# trans=ax.transData.transform +# trans2=fig.transFigure.inverted().transform +# +# piesize=0.4 # this is the image size +# p2=piesize/2.0 +# for n in G: +# xx,yy=trans(pos[n]) # figure coordinates +# xa,ya=trans2((xx,yy)) # axes coordinates +# a = plt.axes([xa-p2,ya-p2, piesize, piesize]) +# a.set_aspect('equal') +# a.imshow(G.nodes[n]['image']) +# a.axis('off') +# ax.axis('off') +# plt.show() diff --git a/test.py b/test.py deleted file mode 100644 index 054de3b7..00000000 --- a/test.py +++ /dev/null @@ -1,97 +0,0 @@ -from helper_battlesnake import * - - -game = { - "game": { - "id": "totally-unique-game-id", - "ruleset": { - "name": "standard", - "version": "v1.1.15", - "settings": { - "foodSpawnChance": 15, - "minimumFood": 1, - "hazardDamagePerTurn": 14 - } - }, - "map": "standard", - "source": "league", - "timeout": 500 - }, - "turn": 14, - "board": { - "height": 11, - "width": 11, - "food": [ - {"x": 5, "y": 5}, - {"x": 9, "y": 0}, - {"x": 2, "y": 6} - ], - "hazards": [ - {"x": 3, "y": 2} - ], - "snakes": [ - { - "id": "snake-508e96ac-94ad-11ea-bb37", - "name": "My Snake", - "health": 54, - "body": [ - {"x": 0, "y": 0}, - {"x": 1, "y": 0}, - {"x": 2, "y": 0} - ], - "latency": "111", - "head": {"x": 0, "y": 0}, - "length": 3, - "shout": "why are we shouting??", - "customizations":{ - "color":"#FF0000", - "head":"pixel", - "tail":"pixel" - } - }, - { - "id": "snake-b67f4906-94ae-11ea-bb37", - "name": "Another Snake", - "health": 16, - "body": [ - {"x": 5, "y": 4}, - {"x": 5, "y": 3}, - {"x": 6, "y": 3}, - {"x": 6, "y": 2} - ], - "latency": "222", - "head": {"x": 5, "y": 4}, - "length": 4, - "shout": "I'm not really sure...", - "customizations":{ - "color":"#26CF04", - "head":"silly", - "tail":"curled" - } - } - ] - }, - "you": { - "id": "snake-508e96ac-94ad-11ea-bb37", - "name": "My Snake", - "health": 54, - "body": [ - {"x": 1, "y": 5}, - {"x": 1, "y": 5}, - {"x": 1, "y": 5} - ], - "latency": "111", - "head": {"x": 1, "y": 5}, - "length": 3, - "shout": "why are we shouting??", - "customizations": { - "color":"#FF0000", - "head":"pixel", - "tail":"pixel" - } - } -} - -a, b = a_star_search(game_state=game, head_loc={"x": 1, "y": 5}, food_loc={"x": 7, "y": 4}) -print(a, b) -print(snake_compass({"x": 1, "y": 5}, a[0])) \ No newline at end of file diff --git a/test_flood_fill.py b/visualise_minimax.py similarity index 100% rename from test_flood_fill.py rename to visualise_minimax.py From 95374ed38a55791c28f7265c82d222bf3d8ecf11 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 04:47:55 +0100 Subject: [PATCH 29/64] chanes12 --- snake_engine.py | 144 ++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 115 insertions(+), 29 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 455ad911..360aca55 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -2,22 +2,22 @@ import itertools import logging import os +import networkx as nx import numpy as np import sys import time from collections import Counter +from networkx_tree import hierarchy_pos +from matplotlib import colors, image as mpimg, pyplot as plt -logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) -# If we're not running locally, then disable any logging -running_locally = "HDPC6511" in os.popen("hostname").read() -if not running_locally: - logging.disable(logging.INFO) - -# tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} +tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} +tree_edges = [] +tree_nodes = [] +tree_node_counter = 1 class Battlesnake: - def __init__(self, game_state): + def __init__(self, game_state, debugging=False): # General game data self.turn = game_state["turn"] self.board_width = game_state["board"]["width"] @@ -72,6 +72,10 @@ def __init__(self, game_state): self.update_board() self.minimax_search_depth = 4 self.peripheral_dim = 3 + self.debugging = debugging + logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) + if not self.debugging: + logging.disable(logging.INFO) def __copy__(self): """Making a deep copy of the game_state dictionary takes too much time, so let's manually build it from @@ -101,7 +105,7 @@ def __copy__(self): "length": self.my_length } new_game_state = {"turn": self.turn, "board": board, "you": you} - return Battlesnake(new_game_state) + return Battlesnake(new_game_state, debugging=self.debugging) def update_board(self): """Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" @@ -276,7 +280,7 @@ def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, so f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") return possible_moves - def minimax_move(self): + def optimal_move(self): """Let's run the minimax algorithm with alpha-beta pruning!""" # Compute the best score of each move using the minimax algorithm with alpha-beta pruning if self.turn <= 4: # Our first 4 moves are super self-explanatory tbh @@ -288,7 +292,26 @@ def minimax_move(self): else: search_depth = self.minimax_search_depth - _, best_move = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) + tree_tracker[search_depth].append(0) + _, best_move, _ = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) + + # Output a visualisation of the minimax decision tree for debugging + if self.debugging: + G = nx.Graph() + node_labels = {} + for node in tree_nodes: + G.add_node(node[0]) + node_labels[node[0]] = node[1] + G.add_edges_from(tree_edges) + pos = hierarchy_pos(G, 0) + edge_colours = [G[u][v]["colour"] for u, v in G.edges()] + edge_widths = [G[u][v]["width"] for u, v in G.edges()] + + fig = plt.figure(figsize=(50, 25)) + nx.draw(G, pos=pos, node_color=["white"] * G.number_of_nodes(), edge_color=edge_colours, width=edge_widths, + labels=node_labels, with_labels=True, node_size=40000, font_size=20) + plt.savefig("minimax_tree.png", bbox_inches="tight", pad_inches=0) + return best_move def is_game_over(self, for_snake_id=None, depth=None): @@ -354,14 +377,22 @@ def heuristic(self, depth_number): else: space_penalty = 0 + # ARE WE TRAPPED??? + trap_space = None + if available_space <= 10: + trap_space = available_space - self.flood_fill(self.my_id, estimate_moves=available_space) + # Shoot we're trapped + if trap_space == 0: + space_penalty = -1e7 + # Estimate the space we have in our peripheral vision available_peripheral = self.flood_fill(self.my_id, confined_area="auto") # # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in # # our vicinity) - # if len(self.opponents) <= 4 \ - # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 4 \ - # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + # if len(self.opponents) <= 3: + # # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ + # # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): # self.peripheral_dim = 4 # available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") # else: @@ -420,11 +451,18 @@ def heuristic(self, depth_number): (self.my_length * length_weight) + \ in_centre * centre_control_weight + \ aggression_weight / (dist_to_enemy + 1) - # (enemy_restriction_weight / (available_enemy_space + 1)) + \ - - return h - - def flood_fill(self, snake_id, confined_area=None, risk_averse=False): +# (enemy_restriction_weight / (available_enemy_space + 1)) + \ + + return h, {"Heur": round(h, 2), + "Space": available_space, + "Penalty": space_penalty, + "Periph": available_peripheral, + "Food Dist": dist_food, + "Enemy Dist": dist_to_enemy, + "Threats": num_threats, + "Length": self.my_length} + + def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_moves=0): """Recursive function to get the total area of the current fill selection. Basically, count how many £ symbols we can fill while avoiding any $, O, and X symbols (obstacles). Confined_area tells the function to do flood fill only on one side of the snake (should be either left/right/up/down).""" @@ -433,6 +471,12 @@ def flood_fill(self, snake_id, confined_area=None, risk_averse=False): # Assume we're doing flood fill for our snake if snake_id == self.my_id: board = copy.deepcopy(self.board) + # Let's simulate entrapment + if estimate_moves > 0: + for snake in self.all_snakes_dict.values(): + tail_removed = snake["body"][-estimate_moves:] + for remove in tail_removed: + board[remove["x"]][remove["y"]] = " " # Let's try to avoid any squares that our enemy can go to if risk_averse: threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] @@ -563,6 +607,30 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") return new_game + @staticmethod + def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data=None, insert_index=None): + global tree_node_counter + global tree_tracker + if add_edges: + # Add the node that we'll be creating the edge to + tree_tracker[depth].append(tree_node_counter) + # Tuple of (node_1, node_2, node_attributes) where the edge is created between node_1 and node_2 + global tree_edges + tree_edges.append((tree_tracker[depth + 1][-1], tree_tracker[depth][-1], {"colour": "k", "width": 1})) + # Now we're going to be on the next node + tree_node_counter += 1 + return len(tree_edges) - 1 + + if add_nodes: + global tree_nodes + if insert_index is not None: + node_move = tree_nodes[insert_index][1] + formatted_dict = str(node_data).replace(", ", "\n").replace("{", "").replace("}", "").replace("'", "") + tree_nodes[insert_index] = (tree_tracker[depth][-1], node_move + "\n" + formatted_dict) + else: + tree_nodes.append((tree_tracker[depth][-1], str(node_data).replace("'", ""))) + return len(tree_nodes) - 1 + def minimax(self, depth, alpha, beta, maximising_snake): """Implement the minimax algorithm with alpha-beta pruning @@ -577,19 +645,21 @@ def minimax(self, depth, alpha, beta, maximising_snake): game_over, still_alive = self.is_game_over(for_snake_id=self.my_id, depth=depth) if not still_alive: logging.info("Our snake died...") - return -1e6 + (self.minimax_search_depth - depth), None # Reward slower deaths + heuristic = -1e6 + (self.minimax_search_depth - depth) # Reward slower deaths + return heuristic, None, {"Heur": heuristic} # Otherwise, if our snake is ALIVE and is the winner :) elif game_over: logging.info("OUR SNAKE WON") - return 1e6 + depth, None # Reward faster kills + heuristic = 1e6 + depth # Reward faster kills + return heuristic, None, heuristic # At the bottom of the decision tree or if we won/lost the game if depth == 0: logging.info("=" * 50) logging.info(f"DEPTH = {depth}") - heuristic = self.heuristic(depth_number=depth) + heuristic, heuristic_data = self.heuristic(depth_number=depth) logging.info(f"Heuristic = {heuristic} at terminal node") - return heuristic, None + return heuristic, None, heuristic_data # Our snake's turn if maximising_snake: @@ -606,16 +676,20 @@ def minimax(self, depth, alpha, beta, maximising_snake): possible_moves = ["down"] best_val, best_move = -np.inf, None + best_node_data, best_edge = None, None for num, move in enumerate(possible_moves): SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) logging.info(f"{len(possible_moves)} CHILD NODES: VISITING {num + 1} OF {len(possible_moves)}") logging.info(f"Running minimax for OUR SNAKE moving {move}") - if running_locally: + if self.debugging: SIMULATED_BOARD_INSTANCE.display_board() clock_in2 = time.time_ns() - node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, False) + edge_added = self.update_tree_visualisation(add_edges=True, depth=depth - 1) + node_added = self.update_tree_visualisation(add_nodes=True, depth=depth - 1, node_data=move) + node_val, node_move, node_data = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, False) + self.update_tree_visualisation(add_nodes=True, depth=depth - 1, node_data=node_data, insert_index=node_added) logging.info("=" * 50) logging.info(f"BACK AT DEPTH = {depth} OUR SNAKE") @@ -624,6 +698,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): # Update best score and best move if np.argmax([best_val, node_val]) == 1: best_move = move + best_node_data, best_edge = node_data, edge_added best_val = max(best_val, node_val) old_alpha = alpha alpha = max(alpha, best_val) @@ -636,8 +711,11 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"PRUNED!!! Alpha = {alpha} >= Beta = {beta}") break + global tree_edges + tree_edges[best_edge][2]["colour"] = "r" + tree_edges[best_edge][2]["width"] = 4 logging.info(f"FINISHED MINIMAX LAYER on our snake in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return best_val, best_move + return best_val, best_move, best_node_data # Opponents' turns else: @@ -703,13 +781,18 @@ def minimax(self, depth, alpha, beta, maximising_snake): clock_in = time.time_ns() best_val, best_move = np.inf, None + best_node_data, best_edge = None, None for num, SIMULATED_BOARD_INSTANCE in enumerate(possible_sims): logging.info(f"{len(possible_sims)} CHILD NODES: VISITING {num + 1} OF {len(possible_sims)}") logging.info(f"Running minimax for OPPONENT SNAKES moving {possible_movesets[num]}") - if running_locally: + if self.debugging: SIMULATED_BOARD_INSTANCE.display_board() clock_in2 = time.time_ns() - node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, True) + edge_added = self.update_tree_visualisation(add_edges=True, depth=depth - 1) + node_added = self.update_tree_visualisation(add_nodes=True, depth=depth - 1, node_data=str(possible_movesets[num])) + node_val, node_move, node_data = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, True) + self.update_tree_visualisation(add_nodes=True, depth=depth - 1, node_data=node_data, + insert_index=node_added) logging.info("=" * 50) logging.info(f"BACK AT DEPTH = {depth} OPPONENT SNAKES") @@ -718,6 +801,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): # Update best score and best move if np.argmin([best_val, node_val]) == 1: best_move = possible_movesets[num] + best_node_data, best_edge = node_data, edge_added best_val = min(best_val, node_val) old_beta = beta beta = min(beta, best_val) @@ -730,8 +814,10 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"PRUNED!!! Beta = {beta} <= Alpha = {alpha}") break + tree_edges[best_edge][2]["colour"] = "r" + tree_edges[best_edge][2]["width"] = 4 logging.info(f"FINISHED MINIMAX LAYER on opponents in {(time.time_ns() - clock_in) // 1000000} ms") - return best_val, best_move + return best_val, best_move, best_node_data # def a_star_search(self, food_loc): # """ From 730a41f02fbb63629eb8da2021c3376b774223d4 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 04:51:23 +0100 Subject: [PATCH 30/64] chanes12 --- requirements.txt | 5 ++++- snake_engine.py | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 19288ee5..f3f4adb1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,6 @@ Flask==2.3.2 -numpy~=1.26.0 \ No newline at end of file +numpy~=1.26.0 +pandas~=2.1.1 +matplotlib~=3.8.0 +networkx~=3.1 \ No newline at end of file diff --git a/snake_engine.py b/snake_engine.py index 360aca55..c23ac3eb 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -2,7 +2,6 @@ import itertools import logging import os -import networkx as nx import numpy as np import sys import time @@ -297,6 +296,7 @@ def optimal_move(self): # Output a visualisation of the minimax decision tree for debugging if self.debugging: + import networkx as nx G = nx.Graph() node_labels = {} for node in tree_nodes: From 475b7b6a3ab6073364368064f70a07ca59648e78 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 04:58:56 +0100 Subject: [PATCH 31/64] main --- main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main.py b/main.py index c2d24eec..d0a248a6 100644 --- a/main.py +++ b/main.py @@ -44,7 +44,7 @@ def move(game_state: typing.Dict) -> typing.Dict: See https://docs.battlesnake.com/api/example-move for available data """ game = Battlesnake(game_state) - optimal_move = game.minimax_move() + optimal_move = game.optimal_move() print(f"MOVE {game_state['turn']}: {optimal_move}") return {"move": optimal_move} From 058c948081d7b227323255a691b241eff89ae5ce Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 06:35:56 +0100 Subject: [PATCH 32/64] HM pt 12 --- networkx_tree.py | 39 ++------------------------------------- snake_engine.py | 10 +++++----- 2 files changed, 7 insertions(+), 42 deletions(-) diff --git a/networkx_tree.py b/networkx_tree.py index 4766c3ba..0da9db73 100644 --- a/networkx_tree.py +++ b/networkx_tree.py @@ -1,10 +1,8 @@ -import matplotlib.pyplot as plt -import matplotlib.image as mpimg import networkx as nx import random -def hierarchy_pos(G, root=None, width=25., vert_gap=0.1, vert_loc=0, leaf_vs_root_factor=1): +def hierarchy_pos(G, root=None, width=25., vert_gap=0.025, vert_loc=0, leaf_vs_root_factor=1): ''' If the graph is a tree this will return the positions to plot this in a hierarchical layout. @@ -26,7 +24,7 @@ def hierarchy_pos(G, root=None, width=25., vert_gap=0.1, vert_loc=0, leaf_vs_roo Based on this, leaf nodes at higher levels get the same space as leaf nodes very deep in the tree. - We use use both of these approaches simultaneously with ``leaf_vs_root_factor`` + We use both of these approaches simultaneously with ``leaf_vs_root_factor`` determining how much of the horizontal space is based on the bottom up or top down approaches. ``0`` gives pure bottom up, while 1 gives pure top down. @@ -125,36 +123,3 @@ def _hierarchy_pos(G, root, leftmost, width, leafdx=2, vert_gap=0.2, vert_loc=0, for node in pos: pos[node] = (pos[node][0] * width / xmax, pos[node][1]) return pos - - -# G = nx.Graph() -# img=mpimg.imread("testgrid.png") -# G.add_node(4,image=img) -# G.add_node("1",image=img) -# G.add_node(2,image=img) -# G.add_node(3,image=img) -# fig=plt.figure(figsize=(12,3)) -# ax=plt.subplot(111) -# G.add_edges_from([("1",2), ("1",3), ("1",4)]) -# pos = hierarchy_pos(G, "1") -# -# nx.draw(G, pos=pos, node_color=["white"] * G.number_of_nodes(), with_labels=True) -# # -# # x,y=pos[5] -# # print(x, y) -# # plt.text(x,y+0.05,s='some text', bbox=dict(facecolor='red', alpha=0.5),horizontalalignment='center') -# -# trans=ax.transData.transform -# trans2=fig.transFigure.inverted().transform -# -# piesize=0.4 # this is the image size -# p2=piesize/2.0 -# for n in G: -# xx,yy=trans(pos[n]) # figure coordinates -# xa,ya=trans2((xx,yy)) # axes coordinates -# a = plt.axes([xa-p2,ya-p2, piesize, piesize]) -# a.set_aspect('equal') -# a.imshow(G.nodes[n]['image']) -# a.axis('off') -# ax.axis('off') -# plt.show() diff --git a/snake_engine.py b/snake_engine.py index c23ac3eb..425f8655 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -87,7 +87,7 @@ def __copy__(self): "body": snake["body"].copy(), "length": snake["length"], "health": snake["health"], - "food_eaten": snake["food_eaten"] + "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else [] }) board = { "height": self.board_height, @@ -282,7 +282,7 @@ def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, so def optimal_move(self): """Let's run the minimax algorithm with alpha-beta pruning!""" # Compute the best score of each move using the minimax algorithm with alpha-beta pruning - if self.turn <= 4: # Our first 4 moves are super self-explanatory tbh + if self.turn < 3: # Our first 3 moves are super self-explanatory tbh search_depth = 1 elif len(self.opponents) > 6: search_depth = 2 # TODO should be risk-averse @@ -592,7 +592,7 @@ def simulate_move(self, move, snake_id, evaluate_deaths=False): lengths = overlapping_snakes[:, 1].astype(int) # Special cases where the snake committed suicide and also killed our snake => don't remove if not (self.my_id in overlapping_snakes[:, 0] - and np.count_nonzero(lengths == self.my_length) > 1): + and np.count_nonzero(lengths == new_game.my_length) > 1): indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() if len(indices_largest_snakes) > 1: winner_id = None @@ -732,7 +732,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): elif len(self.opponents) < 6: search_within = self.board_width // 2 else: - search_within = self.board_width // 2 - 1 + search_within = self.board_width // 2 # Grab possible moves for all opponent snakes opps_nearby = 0 # Counter for opponents in our vicinity @@ -743,7 +743,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): opp_move = ["down"] # Save time by only searching for snakes within close range - if self.manhattan_distance(self.my_head, opp_snake["head"]) < search_within: + if self.manhattan_distance(self.my_head, opp_snake["head"]) <= search_within: opps_moves[opp_id] = opp_move # ALL OF THEM opps_nearby += 1 else: From 55fd6c1963143b599cc274fca21a413edc5243ac Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 07:30:41 +0100 Subject: [PATCH 33/64] No edge kill --- snake_engine.py | 69 ++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 60 insertions(+), 9 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 425f8655..4c5a044b 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -157,6 +157,21 @@ def manhattan_distance(start, end): end = (end["x"], end["y"]) return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) + def our_direction(self, snake_id): + head = self.all_snakes_dict[snake_id]["head"] + neck = self.all_snakes_dict[snake_id]["neck"] + if neck["x"] < head["x"]: + direction = "right" + elif neck["x"] > head["x"]: + direction = "left" + elif neck["y"] < head["y"]: + direction = "up" + elif neck["y"] > head["y"]: + direction = "down" + else: + direction = None + return direction + def peripheral_vision(self, snake_id, direction): """Return [x1, x2] and [y1, y2] of a portion of the board that functions as 'peripheral vision' for the snake""" # Our peripheral field of vision when scanning for moves @@ -165,14 +180,7 @@ def peripheral_vision(self, snake_id, direction): dim = self.peripheral_dim if direction == "auto": # Got to figure out the direction ourselves - if neck["x"] < head["x"]: - direction = "right" - elif neck["x"] > head["x"]: - direction = "left" - elif neck["y"] < head["y"]: - direction = "up" - elif neck["y"] > head["y"]: - direction = "down" + direction = self.our_direction(snake_id) head = neck.copy() # Roll back our head location if direction == "right": # Neck is left of head, don't move left @@ -355,6 +363,44 @@ def is_game_over(self, for_snake_id=None, depth=None): return game_over, snake_still_alive + def edge_kill_detection(self): + if 0 < self.my_head["x"] < self.board_width and 0 < self.my_head["y"] < self.board_height: + return False + + possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) + direction = self.our_direction(self.my_id) + if self.my_head["x"] == 0: + if "right" not in possible_moves: + suspect = [opp_id for opp_id, opp in self.opponents.items() + if opp["head"]["x"] == self.my_head["x"] + 1 and ( + (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) + or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) + )] + if len(suspect) > 0: + diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) + gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to + if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): + return True + if self.my_head["x"] == self.board_width - 1: + if "left" not in possible_moves: + suspect = [opp_id for opp_id, opp in self.opponents.items() + if opp["head"]["x"] == self.my_head["x"] - 1 and ( + (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) + or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) + )] + if len(suspect) > 0: + diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) + gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to + if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): + return True + if self.my_head["y"] == 0: + if "up" not in possible_moves: + return True + if self.my_head["y"] == self.board_height - 1: + if "down" not in possible_moves: + return True + return False + def heuristic(self, depth_number): """Let's figure out a way to evaluate the current board for our snake :) @@ -383,7 +429,12 @@ def heuristic(self, depth_number): trap_space = available_space - self.flood_fill(self.my_id, estimate_moves=available_space) # Shoot we're trapped if trap_space == 0: - space_penalty = -1e7 + space_penalty = -1e7 # We'd prefer getting killed than getting trapped, so penalise this more + + # ARE WE GOING TO GET EDGE-KILLED??? + possible_edged = self.edge_kill_detection() + if possible_edged: + space_penalty = -1e5 # Estimate the space we have in our peripheral vision available_peripheral = self.flood_fill(self.my_id, confined_area="auto") From 721a1ea0afef29e4b17ea4bf8bcd4e5a8d4cff1d Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 16:06:07 +0100 Subject: [PATCH 34/64] Don't get trapped --- snake_engine.py | 40 ++++++++++++++++++++++++++++++---------- 1 file changed, 30 insertions(+), 10 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 4c5a044b..83e4ca51 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -1,6 +1,7 @@ import copy import itertools import logging +import operator import os import numpy as np import sys @@ -464,7 +465,7 @@ def heuristic(self, depth_number): dist_food = self.dist_to_nearest_food() health_flag = True if self.my_health < 40 else False shortest_flag = True if sum([self.my_length <= snake["length"] for snake in self.opponents.values()]) >= min([2, len(self.opponents)]) else False - + longest_flag = True if sum([self.my_length > snake["length"] for snake in self.opponents.values()]) == len(self.opponents) else False # Are we in the centre of the board? Maximise control centre = range(self.board_width // 2 - 2, self.board_width // 2 + 3) @@ -475,12 +476,12 @@ def heuristic(self, depth_number): peripheral_weight = 2 enemy_left_weight = 1000 enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 - food_weight = 250 if health_flag else 175 if shortest_flag else 75 + food_weight = 250 if health_flag else 175 if shortest_flag else 25 if longest_flag else 50 depth_weight = 25 length_weight = 300 centre_control_weight = 10 aggression_weight = 250 if dist_to_enemy > 0 else 0 - threat_proximity_weight = -45 + threat_proximity_weight = -25 logging.info(f"Available space: {available_space}") logging.info(f"Available peripheral: {available_peripheral}") @@ -780,8 +781,8 @@ def minimax(self, depth, alpha, beta, maximising_snake): search_within = self.board_width * self.board_height elif len(self.opponents) <= 3: search_within = self.board_width - elif len(self.opponents) < 6: - search_within = self.board_width // 2 + elif len(self.opponents) <= 5: + search_within = self.board_width // 2 + 1 else: search_within = self.board_width // 2 @@ -794,13 +795,17 @@ def minimax(self, depth, alpha, beta, maximising_snake): opp_move = ["down"] # Save time by only searching for snakes within close range - if self.manhattan_distance(self.my_head, opp_snake["head"]) <= search_within: - opps_moves[opp_id] = opp_move # ALL OF THEM + dist_opp_to_us = self.manhattan_distance(self.my_head, opp_snake["head"]) + if dist_opp_to_us <= search_within: + opps_moves[opp_id] = opp_move # Put all of their moves opps_nearby += 1 else: if len(self.opponents) <= 5: opps_moves[opp_id] = [opp_move[0]] + sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"])) + opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_opps_by_dists.index(pair[0]))) + logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " f"squares of us in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") @@ -810,10 +815,25 @@ def minimax(self, depth, alpha, beta, maximising_snake): all_opp_combos = list(itertools.product(*opps_moves.values())) if len(all_opp_combos) > 2 and len(self.opponents) > 2: logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") - all_opp_combos = all_opp_combos[:2] + cutoff = 3 elif len(all_opp_combos) > 3: - logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") - all_opp_combos = all_opp_combos[:3] + logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 3") + cutoff = 3 + else: + cutoff = 3 + + covered_ids = [list(opps_moves.keys())[0]] + all_opp_combos2 = [] + while len(all_opp_combos2) < cutoff: + combo_counter = 1 + for s_id, s in opps_moves.items(): + if s_id not in covered_ids: + combo_counter = combo_counter * len(s) + index_getter = np.arange(len(covered_ids) - 1, len(all_opp_combos), combo_counter) + getter = [all_opp_combos[i] for i in index_getter] + all_opp_combos2.extend(getter) + + all_opp_combos = all_opp_combos2[:cutoff] possible_movesets = [] possible_sims = [] From c19d16252cc1cfb2b6a01654b7524454c83c5ec0 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 19 Oct 2023 16:22:57 +0100 Subject: [PATCH 35/64] Don't get trapped edge --- snake_engine.py | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 83e4ca51..9831d7da 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -311,6 +311,8 @@ def optimal_move(self): for node in tree_nodes: G.add_node(node[0]) node_labels[node[0]] = node[1] + G.add_node(0) + node_labels[0] = self.display_board(return_string=True) G.add_edges_from(tree_edges) pos = hierarchy_pos(G, 0) edge_colours = [G[u][v]["colour"] for u, v in G.edges()] @@ -365,7 +367,7 @@ def is_game_over(self, for_snake_id=None, depth=None): return game_over, snake_still_alive def edge_kill_detection(self): - if 0 < self.my_head["x"] < self.board_width and 0 < self.my_head["y"] < self.board_height: + if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: return False possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) @@ -822,18 +824,19 @@ def minimax(self, depth, alpha, beta, maximising_snake): else: cutoff = 3 - covered_ids = [list(opps_moves.keys())[0]] - all_opp_combos2 = [] - while len(all_opp_combos2) < cutoff: - combo_counter = 1 - for s_id, s in opps_moves.items(): - if s_id not in covered_ids: - combo_counter = combo_counter * len(s) - index_getter = np.arange(len(covered_ids) - 1, len(all_opp_combos), combo_counter) - getter = [all_opp_combos[i] for i in index_getter] - all_opp_combos2.extend(getter) - - all_opp_combos = all_opp_combos2[:cutoff] + if len(opps_moves) > 0: + covered_ids = [list(opps_moves.keys())[0]] + all_opp_combos2 = [] + while len(all_opp_combos2) < cutoff: + combo_counter = 1 + for s_id, s in opps_moves.items(): + if s_id not in covered_ids: + combo_counter = combo_counter * len(s) + index_getter = np.arange(len(covered_ids) - 1, len(all_opp_combos), combo_counter) + getter = [all_opp_combos[i] for i in index_getter] + all_opp_combos2.extend(getter) + + all_opp_combos = all_opp_combos2[:cutoff] possible_movesets = [] possible_sims = [] From c8abc5520572f98d11c84eb4072526fe092588a4 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Fri, 20 Oct 2023 17:37:45 +0100 Subject: [PATCH 36/64] Final changes here --- battlesnake_test.py | 42 +++++++++++++++++++++++++++--------------- snake_engine.py | 2 -- 2 files changed, 27 insertions(+), 17 deletions(-) diff --git a/battlesnake_test.py b/battlesnake_test.py index d6888818..19769d1f 100644 --- a/battlesnake_test.py +++ b/battlesnake_test.py @@ -1,24 +1,36 @@ -from minimax import * +from snake_engine import * -# game_state = {'game': {'id': 'c05521ea-91ad-4f2f-90d0-1fc2061965d4', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 5, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_WYfcQxBtYbg9DV3YRRg8dKd9', 'name': 'Nightwing', 'latency': '54', 'health': 95, 'body': [{'x': 8, 'y': 7}, {'x': 9, 'y': 7}, {'x': 10, 'y': 7}], 'head': {'x': 8, 'y': 7}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_gWqyHSwhtDxTtQ8mSymfrQvb', 'name': 'viper', 'latency': '500', 'health': 95, 'body': [{'x': 1, 'y': 10}, {'x': 1, 'y': 9}, {'x': 1, 'y': 8}], 'head': {'x': 1, 'y': 10}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#e04c07', 'head': 'missile', 'tail': 'nr-booster'}}, {'id': 'gs_p3WmqtmFvXtqbmbWcWB4JfH8', 'name': 'ricksnek', 'latency': '114', 'health': 99, 'body': [{'x': 5, 'y': 0}, {'x': 4, 'y': 0}, {'x': 4, 'y': 1}, {'x': 4, 'y': 2}], 'head': {'x': 5, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_y9BxCH4tHt4yGvYpTbvPTMSH', 'name': 'Lispy', 'latency': '11', 'health': 97, 'body': [{'x': 6, 'y': 7}, {'x': 6, 'y': 8}, {'x': 6, 'y': 9}, {'x': 6, 'y': 10}], 'head': {'x': 6, 'y': 7}, 'length': 4, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_SJf3kDmFT3hBmjH8GqgbhjBP', 'name': 'InGodWeRust', 'latency': '28', 'health': 97, 'body': [{'x': 10, 'y': 9}, {'x': 9, 'y': 9}, {'x': 8, 'y': 9}, {'x': 8, 'y': 10}], 'head': {'x': 10, 'y': 9}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_89CJrywcHJfy7pg3BcFdVgvX', 'name': 'colin', 'latency': '500', 'health': 95, 'body': [{'x': 1, 'y': 6}, {'x': 1, 'y': 5}, {'x': 1, 'y': 4}], 'head': {'x': 1, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#6600ff', 'head': 'all-seeing', 'tail': 'weight'}}, {'id': 'gs_KwpcQXb3hxXqRpCrc8SgbHcF', 'name': 'danger-noodle', 'latency': '500', 'health': 95, 'body': [{'x': 9, 'y': 6}, {'x': 9, 'y': 5}, {'x': 9, 'y': 4}], 'head': {'x': 9, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 10, 'y': 4}, {'x': 0, 'y': 6}, {'x': 2, 'y': 0}, {'x': 8, 'y': 0}, {'x': 0, 'y': 8}, {'x': 5, 'y': 5}, {'x': 2, 'y': 3}, {'x': 2, 'y': 1}], 'hazards': []}, 'you': {'id': 'gs_WYfcQxBtYbg9DV3YRRg8dKd9', 'name': 'Nightwing', 'latency': '54', 'health': 95, 'body': [{'x': 8, 'y': 7}, {'x': 9, 'y': 7}, {'x': 10, 'y': 7}], 'head': {'x': 8, 'y': 7}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} # PROBLEM -# game_state = {'game': {'id': '2babaad2-8d7d-42fc-b107-c4848c7c20dc', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 144, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_78cQV3vbT8CfDPSCQytS9xbQ', 'name': 'Nightwing', 'latency': '52', 'health': 76, 'body': [{'x': 9, 'y': 7}, {'x': 9, 'y': 8}, {'x': 9, 'y': 9}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}, {'x': 3, 'y': 9}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 2, 'y': 9}, {'x': 2, 'y': 8}, {'x': 1, 'y': 8}, {'x': 1, 'y': 7}, {'x': 2, 'y': 7}], 'head': {'x': 9, 'y': 7}, 'length': 18, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_MTDX6KSj9TbtTktmy9HkMFVC', 'name': 'InGodWeRust', 'latency': '26', 'health': 96, 'body': [{'x': 9, 'y': 3}, {'x': 9, 'y': 2}, {'x': 9, 'y': 1}, {'x': 9, 'y': 0}, {'x': 8, 'y': 0}, {'x': 8, 'y': 1}, {'x': 8, 'y': 2}, {'x': 7, 'y': 2}, {'x': 7, 'y': 3}, {'x': 6, 'y': 3}, {'x': 6, 'y': 4}, {'x': 6, 'y': 5}, {'x': 6, 'y': 6}, {'x': 7, 'y': 6}, {'x': 7, 'y': 7}, {'x': 7, 'y': 8}, {'x': 7, 'y': 9}], 'head': {'x': 9, 'y': 3}, 'length': 17, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}], 'food': [{'x': 3, 'y': 3}, {'x': 9, 'y': 4}], 'hazards': []}, 'you': {'id': 'gs_78cQV3vbT8CfDPSCQytS9xbQ', 'name': 'Nightwing', 'latency': '52', 'health': 76, 'body': [{'x': 9, 'y': 7}, {'x': 9, 'y': 8}, {'x': 9, 'y': 9}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}, {'x': 3, 'y': 9}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 2, 'y': 9}, {'x': 2, 'y': 8}, {'x': 1, 'y': 8}, {'x': 1, 'y': 7}, {'x': 2, 'y': 7}], 'head': {'x': 9, 'y': 7}, 'length': 18, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game_state = {'game': {'id': 'fd6dc64e-244e-4744-a2d2-15dbb8adbc29', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 114, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_39MfCDjtdBpQ7fyWMJxTQdqB', 'name': 'Lispy', 'latency': '10', 'health': 98, 'body': [{'x': 10, 'y': 2}, {'x': 10, 'y': 1}, {'x': 10, 'y': 0}, {'x': 9, 'y': 0}, {'x': 8, 'y': 0}, {'x': 7, 'y': 0}, {'x': 6, 'y': 0}, {'x': 5, 'y': 0}, {'x': 5, 'y': 1}, {'x': 4, 'y': 1}, {'x': 3, 'y': 1}, {'x': 2, 'y': 1}, {'x': 2, 'y': 2}, {'x': 2, 'y': 3}], 'head': {'x': 10, 'y': 2}, 'length': 14, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_tfVDkxJgKwJrHPQ6yhrWHxBD', 'name': 'ricksnek', 'latency': '101', 'health': 85, 'body': [{'x': 4, 'y': 8}, {'x': 4, 'y': 7}, {'x': 4, 'y': 6}, {'x': 4, 'y': 5}, {'x': 5, 'y': 5}, {'x': 5, 'y': 6}, {'x': 6, 'y': 6}, {'x': 7, 'y': 6}, {'x': 8, 'y': 6}, {'x': 8, 'y': 5}, {'x': 7, 'y': 5}, {'x': 7, 'y': 4}, {'x': 6, 'y': 4}], 'head': {'x': 4, 'y': 8}, 'length': 13, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_4HcJFdfSH9vcPMPRtX7Fy4WB', 'name': 'Nightwing', 'latency': '121', 'health': 60, 'body': [{'x': 7, 'y': 7}, {'x': 6, 'y': 7}, {'x': 6, 'y': 8}, {'x': 6, 'y': 9}, {'x': 6, 'y': 10}, {'x': 7, 'y': 10}, {'x': 8, 'y': 10}, {'x': 9, 'y': 10}, {'x': 9, 'y': 9}], 'head': {'x': 7, 'y': 7}, 'length': 9, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_fSqKx9mk7hw9qQ6y7gbpdWT3', 'name': 'danger-noodle', 'latency': '15', 'health': 43, 'body': [{'x': 2, 'y': 0}, {'x': 1, 'y': 0}, {'x': 1, 'y': 1}, {'x': 1, 'y': 2}, {'x': 1, 'y': 3}], 'head': {'x': 2, 'y': 0}, 'length': 5, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 0, 'y': 8}, {'x': 10, 'y': 7}, {'x': 0, 'y': 4}], 'hazards': []}, 'you': {'id': 'gs_4HcJFdfSH9vcPMPRtX7Fy4WB', 'name': 'Nightwing', 'latency': '121', 'health': 60, 'body': [{'x': 7, 'y': 7}, {'x': 6, 'y': 7}, {'x': 6, 'y': 8}, {'x': 6, 'y': 9}, {'x': 6, 'y': 10}, {'x': 7, 'y': 10}, {'x': 8, 'y': 10}, {'x': 9, 'y': 10}, {'x': 9, 'y': 9}], 'head': {'x': 7, 'y': 7}, 'length': 9, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game_state = {'game': {'id': '6737333a-2a0c-41d1-b45e-72d60caf6f1f', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 3, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_pRdJRXch6bvRtbVfgvRTt7YY', 'name': 'Lispy', 'latency': '10', 'health': 99, 'body': [{'x': 9, 'y': 8}, {'x': 10, 'y': 8}, {'x': 10, 'y': 9}, {'x': 9, 'y': 9}], 'head': {'x': 9, 'y': 8}, 'length': 4, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_yvCT6PgMSchgXCXQGYvXGkVY', 'name': 'InGodWeRust', 'latency': '29', 'health': 99, 'body': [{'x': 7, 'y': 0}, {'x': 8, 'y': 0}, {'x': 9, 'y': 0}, {'x': 9, 'y': 1}], 'head': {'x': 7, 'y': 0}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_Hq9jP7MwhgpQ34mfS3vdkB3Q', 'name': 'ricksnek', 'latency': '177', 'health': 97, 'body': [{'x': 1, 'y': 8}, {'x': 2, 'y': 8}, {'x': 2, 'y': 9}], 'head': {'x': 1, 'y': 8}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_YtDRmKJdmMXMCCGQ89MRJm8Y', 'name': 'colin', 'latency': '29', 'health': 97, 'body': [{'x': 2, 'y': 3}, {'x': 2, 'y': 2}, {'x': 1, 'y': 2}], 'head': {'x': 2, 'y': 3}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#6600ff', 'head': 'all-seeing', 'tail': 'weight'}}, {'id': 'gs_QvYJ8Mkb7MgMKQPMvRGFkWXR', 'name': 'Nightwing', 'latency': '78', 'health': 99, 'body': [{'x': 0, 'y': 7}, {'x': 0, 'y': 6}, {'x': 1, 'y': 6}, {'x': 1, 'y': 5}], 'head': {'x': 0, 'y': 7}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_SXSMyXgYqx8fy6T6bHq3Qv7D', 'name': 'viper', 'latency': '25', 'health': 97, 'body': [{'x': 5, 'y': 6}, {'x': 5, 'y': 7}, {'x': 5, 'y': 8}], 'head': {'x': 5, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#e04c07', 'head': 'missile', 'tail': 'nr-booster'}}, {'id': 'gs_MRYqXdQRJVPHm3d67Rhc49GK', 'name': 'tsnake', 'latency': '18', 'health': 97, 'body': [{'x': 5, 'y': 2}, {'x': 6, 'y': 2}, {'x': 6, 'y': 1}], 'head': {'x': 5, 'y': 2}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#ff66cc', 'head': 'scarf', 'tail': 'mystic-moon'}}, {'id': 'gs_fmW8p7d9Y4ttXHQWQMGWTMRH', 'name': 'danger-noodle', 'latency': '15', 'health': 97, 'body': [{'x': 8, 'y': 7}, {'x': 9, 'y': 7}, {'x': 9, 'y': 6}], 'head': {'x': 8, 'y': 7}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 0, 'y': 8}, {'x': 0, 'y': 2}, {'x': 4, 'y': 10}, {'x': 4, 'y': 0}, {'x': 10, 'y': 4}, {'x': 5, 'y': 5}], 'hazards': []}, 'you': {'id': 'gs_QvYJ8Mkb7MgMKQPMvRGFkWXR', 'name': 'Nightwing', 'latency': '78', 'health': 99, 'body': [{'x': 0, 'y': 7}, {'x': 0, 'y': 6}, {'x': 1, 'y': 6}, {'x': 1, 'y': 5}], 'head': {'x': 0, 'y': 7}, 'length': 4, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game_state={'game': {'id': '70055471-751e-469e-b03a-62c4ffd4b565', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 33, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_XYmQ7dJVccqMVVCTp9t4w9cM', 'name': 'Lispy', 'latency': '30', 'health': 89, 'body': [{'x': 7, 'y': 8}, {'x': 8, 'y': 8}, {'x': 8, 'y': 7}, {'x': 7, 'y': 7}, {'x': 6, 'y': 7}, {'x': 6, 'y': 6}, {'x': 6, 'y': 5}, {'x': 7, 'y': 5}], 'head': {'x': 7, 'y': 8}, 'length': 8, 'shout': 'Vamos', 'squad': '', 'customizations': {'color': '#4a412a', 'head': 'shades', 'tail': 'sharp'}}, {'id': 'gs_r9hg6mtXWWdwBSYSb3hm763G', 'name': 'InGodWeRust', 'latency': '23', 'health': 75, 'body': [{'x': 5, 'y': 8}, {'x': 4, 'y': 8}, {'x': 3, 'y': 8}, {'x': 2, 'y': 8}, {'x': 1, 'y': 8}, {'x': 1, 'y': 7}], 'head': {'x': 5, 'y': 8}, 'length': 6, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_jYvDkwPMbJHHhfrhpccyXRgC', 'name': 'ricksnek', 'latency': '113', 'health': 83, 'body': [{'x': 3, 'y': 6}, {'x': 4, 'y': 6}, {'x': 4, 'y': 5}, {'x': 4, 'y': 4}, {'x': 4, 'y': 3}], 'head': {'x': 3, 'y': 6}, 'length': 5, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}, {'id': 'gs_wb6TJ9YbYRhm4CymSj3mSqrM', 'name': 'Nightwing', 'latency': '49', 'health': 95, 'body': [{'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 1, 'y': 10}, {'x': 0, 'y': 10}, {'x': 0, 'y': 9}], 'head': {'x': 5, 'y': 10}, 'length': 7, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_PP7MTmjVSFtJqqddwChthyGD', 'name': 'danger-noodle', 'latency': '15', 'health': 67, 'body': [{'x': 9, 'y': 6}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}], 'head': {'x': 9, 'y': 6}, 'length': 3, 'shout': '', 'squad': '', 'customizations': {'color': '#1f9490', 'head': 'default', 'tail': 'default'}}], 'food': [{'x': 6, 'y': 10}], 'hazards': []}, 'you': {'id': 'gs_wb6TJ9YbYRhm4CymSj3mSqrM', 'name': 'Nightwing', 'latency': '49', 'health': 95, 'body': [{'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 3, 'y': 10}, {'x': 2, 'y': 10}, {'x': 1, 'y': 10}, {'x': 0, 'y': 10}, {'x': 0, 'y': 9}], 'head': {'x': 5, 'y': 10}, 'length': 7, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game_state = {'game': {'id': '9043a07a-ce19-4114-a22c-b99f2df9c52a', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 283, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_cb34P3qtR3gMJpmRfm6xPdmW', 'name': 'InGodWeRust', 'latency': '36', 'health': 99, 'body': [{'x': 5, 'y': 2}, {'x': 5, 'y': 3}, {'x': 6, 'y': 3}, {'x': 7, 'y': 3}, {'x': 7, 'y': 2}, {'x': 8, 'y': 2}, {'x': 8, 'y': 1}, {'x': 8, 'y': 0}, {'x': 7, 'y': 0}, {'x': 6, 'y': 0}, {'x': 5, 'y': 0}, {'x': 4, 'y': 0}, {'x': 3, 'y': 0}, {'x': 2, 'y': 0}, {'x': 2, 'y': 1}, {'x': 2, 'y': 2}, {'x': 1, 'y': 2}, {'x': 1, 'y': 1}, {'x': 0, 'y': 1}, {'x': 0, 'y': 2}, {'x': 0, 'y': 3}, {'x': 0, 'y': 4}, {'x': 0, 'y': 5}, {'x': 1, 'y': 5}, {'x': 1, 'y': 4}, {'x': 1, 'y': 3}, {'x': 2, 'y': 3}, {'x': 3, 'y': 3}], 'head': {'x': 5, 'y': 2}, 'length': 28, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_FpTKyQdH33y8XkkyJCrKwWj9', 'name': 'Nightwing', 'latency': '166', 'health': 78, 'body': [{'x': 3, 'y': 4}, {'x': 4, 'y': 4}, {'x': 5, 'y': 4}, {'x': 6, 'y': 4}, {'x': 6, 'y': 5}, {'x': 7, 'y': 5}, {'x': 8, 'y': 5}, {'x': 9, 'y': 5}, {'x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 10, 'y': 9}, {'x': 10, 'y': 10}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}], 'head': {'x': 3, 'y': 4}, 'length': 23, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}], 'food': [{'x': 1, 'y': 0}, {'x': 5, 'y': 1}], 'hazards': []}, 'you': {'id': 'gs_FpTKyQdH33y8XkkyJCrKwWj9', 'name': 'Nightwing', 'latency': '166', 'health': 78, 'body': [{'x': 3, 'y': 4}, {'x': 4, 'y': 4}, {'x': 5, 'y': 4}, {'x': 6, 'y': 4}, {'x': 6, 'y': 5}, {'x': 7, 'y': 5}, {'x': 8, 'y': 5}, {'x': 9, 'y': 5}, {'x': 9, 'y': 4}, {'x': 10, 'y': 4}, {'x': 10, 'y': 5}, {'x': 10, 'y': 6}, {'x': 10, 'y': 7}, {'x': 10, 'y': 8}, {'x': 10, 'y': 9}, {'x': 10, 'y': 10}, {'x': 9, 'y': 10}, {'x': 8, 'y': 10}, {'x': 7, 'y': 10}, {'x': 6, 'y': 10}, {'x': 5, 'y': 10}, {'x': 4, 'y': 10}, {'x': 4, 'y': 9}], 'head': {'x': 3, 'y': 4}, 'length': 23, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game_state = {'game': {'id': 'a0246956-0611-497d-a852-635b3bba4ee8', 'ruleset': {'name': 'standard', 'version': 'v1.2.3', 'settings': {'foodSpawnChance': 15, 'minimumFood': 1, 'hazardDamagePerTurn': 0, 'hazardMap': '', 'hazardMapAuthor': '', 'royale': {'shrinkEveryNTurns': 0}, 'squad': {'allowBodyCollisions': False, 'sharedElimination': False, 'sharedHealth': False, 'sharedLength': False}}}, 'map': 'standard', 'timeout': 500, 'source': 'custom'}, 'turn': 91, 'board': {'height': 11, 'width': 11, 'snakes': [{'id': 'gs_XjXd4v4w8yRYfTXpqYMpVjmX', 'name': 'Nightwing', 'latency': '500', 'health': 88, 'body': [{'x': 6, 'y': 3}, {'x': 6, 'y': 2}, {'x': 6, 'y': 1}, {'x': 7, 'y': 1}, {'x': 8, 'y': 1}, {'x': 9, 'y': 1}, {'x': 9, 'y': 2}, {'x': 9, 'y': 3}], 'head': {'x': 6, 'y': 3}, 'length': 8, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}, {'id': 'gs_F7y7pM3vptdhTJhQd8YXYh3b', 'name': 'InGodWeRust', 'latency': '26', 'health': 96, 'body': [{'x': 1, 'y': 8}, {'x': 1, 'y': 7}, {'x': 0, 'y': 7}, {'x': 0, 'y': 6}, {'x': 0, 'y': 5}, {'x': 1, 'y': 5}, {'x': 2, 'y': 5}, {'x': 3, 'y': 5}, {'x': 4, 'y': 5}, {'x': 5, 'y': 5}, {'x': 6, 'y': 5}, {'x': 6, 'y': 6}], 'head': {'x': 1, 'y': 8}, 'length': 12, 'shout': '', 'squad': '', 'customizations': {'color': '#b7410e', 'head': 'sleepy', 'tail': 'offroad'}}, {'id': 'gs_9PptFR4y7BmP6cX9rrRTFytP', 'name': 'ricksnek', 'latency': '99', 'health': 96, 'body': [{'x': 8, 'y': 9}, {'x': 9, 'y': 9}, {'x': 9, 'y': 8}, {'x': 10, 'y': 8}, {'x': 10, 'y': 7}, {'x': 9, 'y': 7}, {'x': 8, 'y': 7}, {'x': 7, 'y': 7}, {'x': 6, 'y': 7}], 'head': {'x': 8, 'y': 9}, 'length': 9, 'shout': '', 'squad': '', 'customizations': {'color': '#00ff00', 'head': 'caffeine', 'tail': 'coffee'}}], 'food': [{'x': 1, 'y': 9}], 'hazards': []}, 'you': {'id': 'gs_XjXd4v4w8yRYfTXpqYMpVjmX', 'name': 'Nightwing', 'latency': '500', 'health': 88, 'body': [{'x': 6, 'y': 3}, {'x': 6, 'y': 2}, {'x': 6, 'y': 1}, {'x': 7, 'y': 1}, {'x': 8, 'y': 1}, {'x': 9, 'y': 1}, {'x': 9, 'y': 2}, {'x': 9, 'y': 3}], 'head': {'x': 6, 'y': 3}, 'length': 8, 'shout': '', 'squad': '', 'customizations': {'color': '#3333ff', 'head': 'ski', 'tail': 'mystic-moon'}}} -game = Battlesnake(game_state) +# Takes too long +game_state = {"game":{"id":"1940ebec-c2f4-472e-96db-ad5e659b4f7d","ruleset":{"name":"standard","version":"cli","settings":{"foodSpawnChance":15,"minimumFood":1,"hazardDamagePerTurn":14,"hazardMap":"","hazardMapAuthor":"","royale":{"shrinkEveryNTurns":25},"squad":{"allowBodyCollisions":False,"sharedElimination":False,"sharedHealth":False,"sharedLength":False}}},"map":"standard","timeout":500,"source":""},"turn":14,"board":{"height":11,"width":11,"snakes":[{"id":"262d660a-3a3b-4e78-994f-5561814d9eb1","name":"ricksnek2","latency":"131","health":90,"body":[{"x":5,"y":3},{"x":6,"y":3},{"x":7,"y":3},{"x":7,"y":4}],"head":{"x":5,"y":3},"length":4,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}},{"id":"7bb6b728-6e52-45c9-adeb-fc103c09d5f7","name":"Nightwing","latency":"513","health":96,"body":[{"x":2,"y":4},{"x":3,"y":4},{"x":4,"y":4},{"x":5,"y":4},{"x":5,"y":5}],"head":{"x":2,"y":4},"length":5,"shout":"","squad":"","customizations":{"color":"#3333ff","head":"ski","tail":"mystic-moon"}},{"id":"d14b067d-9758-4a50-9f49-03af72175426","name":"ricksnek","latency":"119","health":88,"body":[{"x":5,"y":9},{"x":5,"y":8},{"x":6,"y":8},{"x":6,"y":7}],"head":{"x":5,"y":9},"length":4,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}}],"food":[{"x":0,"y":9},{"x":8,"y":2}],"hazards":[]},"you":{"id":"7bb6b728-6e52-45c9-adeb-fc103c09d5f7","name":"Nightwing","latency":"513","health":96,"body":[{"x":2,"y":4},{"x":3,"y":4},{"x":4,"y":4},{"x":5,"y":4},{"x":5,"y":5}],"head":{"x":2,"y":4},"length":5,"shout":"","squad":"","customizations":{"color":"#3333ff","head":"ski","tail":"mystic-moon"}}} +# WRONG FOOD DISTANCE +game_state = 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+ +game = Battlesnake(game_state, debugging=True) print("STARTING POSITION") game.display_board() - -# l = game.flood_fill(game.my_id, confined_area="left") -# u = game.flood_fill(game.my_id, confined_area="up") -# r = game.flood_fill(game.my_id, confined_area="right") -# print(l, u, r) -next_move = game.minimax_move() +next_move = game.optimal_move() print(next_move) # # import cProfile, pstats diff --git a/snake_engine.py b/snake_engine.py index 9831d7da..9d35fac9 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -1,8 +1,6 @@ import copy import itertools import logging -import operator -import os import numpy as np import sys import time From 64aca086d1a8165fe15218082346f4d32e37b62a Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 23 Oct 2023 14:08:08 +0100 Subject: [PATCH 37/64] Initial draft of repo --- visualise_minimax.py => a_star_search_test.py | 268 +++--- battlesnake.exe | Bin 0 -> 8503808 bytes helper_battlesnake.py | 281 ------- main_fr.py | 85 -- minimax.py | 788 ------------------ tests.py | 24 + 6 files changed, 158 insertions(+), 1288 deletions(-) rename visualise_minimax.py => a_star_search_test.py (96%) create mode 100644 battlesnake.exe delete mode 100644 helper_battlesnake.py delete mode 100644 main_fr.py delete mode 100644 minimax.py create mode 100644 tests.py diff --git a/visualise_minimax.py b/a_star_search_test.py similarity index 96% rename from visualise_minimax.py rename to a_star_search_test.py index 3e7d3a03..3ef85e5c 100644 --- a/visualise_minimax.py +++ b/a_star_search_test.py @@ -1,135 +1,135 @@ -game = { - "game": { - "id": "totally-unique-game-id", - "ruleset": { - "name": "standard", - "version": "v1.1.15", - "settings": { - "foodSpawnChance": 15, - "minimumFood": 1, - "hazardDamagePerTurn": 14 - } - }, - "map": "standard", - "source": "league", - "timeout": 500 - }, - "turn": 14, - "board": { - "height": 11, - "width": 11, - "food": [ - {"x": 2, "y": 0}, - {"x": 1, "y": 9}, - {"x": 4, "y": 6} - ], - "hazards": [ - {"x": 3, "y": 2} - ], - "snakes": [ - { - "id": "snake-508e96ac-94ad-11ea-bb37", - "name": "My Snake", - "health": 54, - "body": [ - {"x": 5, "y": 4}, - {"x": 5, "y": 5}, - {"x": 5, "y": 6}, - {"x": 5, "y": 7}, - {"x": 5, "y": 8}, - {"x": 5, "y": 9}, - {"x": 5, "y": 10}, - # {"x": 5, "y": 7}, - # {"x": 5, "y": 8}, - # {"x": 4, "y": 9} - ], - "latency": "111", - "head": {"x": 0, "y": 0}, - "length": 3, - "shout": "why are we shouting??", - "customizations":{ - "color":"#FF0000", - "head":"pixel", - "tail":"pixel" - } - }, - # { - # "id": "snake-b67f4906-94ae-11ea-bb37", - # "name": "Another Snake", - # "health": 16, - # "body": [ - # {"x": 5, "y": 4}, - # {"x": 5, "y": 3}, - # {"x": 6, "y": 3}, - # {"x": 6, "y": 2} - # ], - # "latency": "222", - # "head": {"x": 5, "y": 4}, - # "length": 4, - # "shout": "I'm not really sure...", - # "customizations":{ - # "color":"#26CF04", - # "head":"silly", - # "tail":"curled" - # } - # } - ] - }, - "you": { - "id": "snake-508e96ac-94ad-11ea-bb37", - "name": "My Snake", - "health": 54, - "body": [ - {"x": 1, "y": 0}, - {"x": 1, "y": 1}, - {"x": 1, "y": 2}, - {"x": 1, "y": 3}, - {"x": 1, "y": 4}, - {"x": 2, "y": 4}, - {"x": 3, "y": 4}, - {"x": 4, "y": 4}, - {"x": 4, "y": 3}, - {"x": 4, "y": 2}, - {"x": 4, "y": 1}, - {"x": 5, "y": 1}, - {"x": 5, "y": 0}, - {"x": 6, "y": 0}, - ], - "latency": "111", - "head": {"x": 1, "y": 0}, - "length": 3, - "shout": "why are we shouting??", - "customizations": { - "color":"#FF0000", - "head":"pixel", - "tail":"pixel" - } - } -} - - -from helper_battlesnake import * - -my_head = {"x": 1, "y": 0} -safe_moves = obvious_moves(game, my_head) -for safe in safe_moves: - f = flood_fill(game, look_ahead(my_head, safe)) - print(safe, f) -print("=" * 50) - -food = game['board']['food'] -food_dist = [] -food_moves = [] -# Big idea: loop through all food and find the shortest path using A* search -for food_loc in food: - best_path, best_dist = a_star_search(game, my_head.copy(), food_loc) - print(f"Food: {food_loc}") - print(f"Shortest dist: {best_dist}") - if best_path is not None: - food_dist.append(best_dist) - lesgo = snake_compass(my_head, best_path[-2]) - food_moves.append(lesgo) - - f = flood_fill(game, look_ahead(my_head, lesgo)) - print(f"Direction {lesgo} at {f}") +game = { + "game": { + "id": "totally-unique-game-id", + "ruleset": { + "name": "standard", + "version": "v1.1.15", + "settings": { + "foodSpawnChance": 15, + "minimumFood": 1, + "hazardDamagePerTurn": 14 + } + }, + "map": "standard", + "source": "league", + "timeout": 500 + }, + "turn": 14, + "board": { + "height": 11, + "width": 11, + "food": [ + {"x": 2, "y": 0}, + {"x": 1, "y": 9}, + {"x": 4, "y": 6} + ], + "hazards": [ + {"x": 3, "y": 2} + ], + "snakes": [ + { + "id": "snake-508e96ac-94ad-11ea-bb37", + "name": "My Snake", + "health": 54, + "body": [ + {"x": 5, "y": 4}, + {"x": 5, "y": 5}, + {"x": 5, "y": 6}, + {"x": 5, "y": 7}, + {"x": 5, "y": 8}, + {"x": 5, "y": 9}, + {"x": 5, "y": 10}, + # {"x": 5, "y": 7}, + # {"x": 5, "y": 8}, + # {"x": 4, "y": 9} + ], + "latency": "111", + "head": {"x": 0, "y": 0}, + "length": 3, + "shout": "why are we shouting??", + "customizations":{ + "color":"#FF0000", + "head":"pixel", + "tail":"pixel" + } + }, + # { + # "id": "snake-b67f4906-94ae-11ea-bb37", + # "name": "Another Snake", + # "health": 16, + # "body": [ + # {"x": 5, "y": 4}, + # {"x": 5, "y": 3}, + # {"x": 6, "y": 3}, + # {"x": 6, "y": 2} + # ], + # "latency": "222", + # "head": {"x": 5, "y": 4}, + # "length": 4, + # "shout": "I'm not really sure...", + # "customizations":{ + # "color":"#26CF04", + # "head":"silly", + # "tail":"curled" + # } + # } + ] + }, + "you": { + "id": "snake-508e96ac-94ad-11ea-bb37", + "name": "My Snake", + "health": 54, + "body": [ + {"x": 1, "y": 0}, + {"x": 1, "y": 1}, + {"x": 1, "y": 2}, + {"x": 1, "y": 3}, + {"x": 1, "y": 4}, + {"x": 2, "y": 4}, + {"x": 3, "y": 4}, + {"x": 4, "y": 4}, + {"x": 4, "y": 3}, + {"x": 4, "y": 2}, + {"x": 4, "y": 1}, + {"x": 5, "y": 1}, + {"x": 5, "y": 0}, + {"x": 6, "y": 0}, + ], + "latency": "111", + "head": {"x": 1, "y": 0}, + "length": 3, + "shout": "why are we shouting??", + "customizations": { + "color":"#FF0000", + "head":"pixel", + "tail":"pixel" + } + } +} + + +from helper_battlesnake import * + +my_head = {"x": 1, "y": 0} +safe_moves = obvious_moves(game, my_head) +for safe in safe_moves: + f = flood_fill(game, look_ahead(my_head, safe)) + print(safe, f) +print("=" * 50) + +food = game['board']['food'] +food_dist = [] +food_moves = [] +# Big idea: loop through all food and find the shortest path using A* search +for food_loc in food: + best_path, best_dist = a_star_search(game, my_head.copy(), food_loc) + print(f"Food: {food_loc}") + print(f"Shortest dist: {best_dist}") + if best_path is not None: + food_dist.append(best_dist) + lesgo = snake_compass(my_head, best_path[-2]) + food_moves.append(lesgo) + + f = flood_fill(game, look_ahead(my_head, lesgo)) + print(f"Direction {lesgo} at {f}") print(best_dist * 11 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--git a/helper_battlesnake.py b/helper_battlesnake.py deleted file mode 100644 index 22b80a8f..00000000 --- a/helper_battlesnake.py +++ /dev/null @@ -1,281 +0,0 @@ -import numpy as np -import typing - - -def look_ahead(head, move): - """Given a possible move, return the snake's new coordinates if it were headed that way""" - snake_head = head.copy() - if move == "up": - snake_head["y"] += 1 - if move == "down": - snake_head["y"] -= 1 - if move == "left": - snake_head["x"] -= 1 - if move == "right": - snake_head["x"] += 1 - return snake_head - - -def snake_compass(head_loc: typing.Dict, next_loc: typing.Dict): - """ - Given the snake's head and an adjacent square, return either left/right/up/down - """ - if head_loc["x"] + 1 == next_loc["x"] and head_loc["y"] == next_loc["y"]: - return "right" - if head_loc["x"] - 1 == next_loc["x"] and head_loc["y"] == next_loc["y"]: - return "left" - if head_loc["x"] == next_loc["x"] and head_loc["y"] + 1 == next_loc["y"]: - return "up" - if head_loc["x"] == next_loc["x"] and head_loc["y"] - 1 == next_loc["y"]: - return "down" - -def manhattan_distance(start: typing.Dict, end: typing.Dict): - """ - Return the Manhattan distance for two positions - """ - start = (start["x"], start["y"]) - end = (end["x"], end["y"]) - return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) - - -def refresh_safe_moves(move_dict): - """ - Return any remaining safe moves - """ - safe_moves = [] - for move, isSafe in move_dict.items(): - if isSafe: - safe_moves.append(move) - return safe_moves - - -def obvious_moves(game_state: typing.Dict, my_head: typing.Dict, my_neck=None, risk_averse=True): - """ - Update the move dictionary and mark unsafe directions as False - """ - is_move_safe = {"up": True, "down": True, "left": True, "right": True} - - my_length = game_state["you"]["length"] - if my_neck is None: - my_neck = game_state["you"]["body"][1] # Coordinates of your "neck" - # print(f"Head location: {str(my_head)}") - - if my_neck["x"] < my_head["x"]: # Neck is left of head, don't move left - is_move_safe["left"] = False - # print("Neck is left of head, don't move left") - elif my_neck["x"] > my_head["x"]: # Neck is right of head, don't move right - is_move_safe["right"] = False - # print("Neck is right of head, don't move right") - elif my_neck["y"] < my_head["y"]: # Neck is below head, don't move down - is_move_safe["down"] = False - # print("Neck is below head, don't move down") - elif my_neck["y"] > my_head["y"]: # Neck is above head, don't move up - is_move_safe["up"] = False - # print("Neck is above head, don't move up") - - # Prevent your Battlesnake from moving out of bounds - board_width = game_state['board']['width'] - board_height = game_state['board']['height'] - - if my_head["x"] == 0: - is_move_safe["left"] = False - # print("Out of bounds, can't move left") - if my_head["x"] + 1 == board_height: - is_move_safe["right"] = False - # print("Out of bounds, can't move right") - if my_head["y"] == 0: - is_move_safe["down"] = False - # print("Out of bounds, can't move down") - if my_head["y"] + 1 == board_width: - is_move_safe["up"] = False - # print("Out of bounds, can't move up") - - # Prevent your Battlesnake from colliding with itself - my_body = game_state['you']['body'] - for direction in refresh_safe_moves(is_move_safe): - possible_move = look_ahead(my_head, direction) - if possible_move in my_body: - is_move_safe[direction] = False - # print(f"Body in the way, can't move {direction}") - # print(f"Headed towards: {str(possible_move)}") - # print(f"Body: {str(my_body)}") - - # Prevent your Battlesnake from colliding with other Battlesnakes - opponents = game_state['board']['snakes'] - for opponent in opponents: - if opponent["id"] == game_state['you']["id"]: # Ignore yourself - continue - - opponent_locs = opponent["body"] - opponent_head = opponent_locs[0] - for direction in ["up", "down", "left", "right"]: - possible_move = look_ahead(my_head, direction) - # Avoid any possible encounters with any future opponent move - if risk_averse and my_length < opponent["length"]: - opponent_moves = [look_ahead(opponent_head, direction) for direction in ["up", "down", "left", "right"]] - if possible_move in opponent_moves: - is_move_safe[direction] = False - # Avoid running into the opponent snake - if possible_move in opponent_locs: - is_move_safe[direction] = False - # print(f"Opponent snake in the way, can't move {direction}") - # print(f"Headed towards: {str(possible_move)}") - # print(f"Opponent: {str(opponent_locs)}") - - return refresh_safe_moves(is_move_safe) - - -class Node: - def __init__(self, location, parent=None, f=0, g=0, h=0): - self.location = location - self.neighbour_nodes = [] - self.parent = parent - self.f = f # The cost from the start node to the goal node - self.g = g # The cost from the start node to the current node - self.h = h # The cost from the current node to the goal node using a heuristic - - def __repr__(self): - return str(self.location) - - -def a_star_search(game_state, my_head, food_loc): - """ - Implement the A* search algorithm to find the shortest path to food - """ - head_loc = my_head.copy() - open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet - closed_list = [] # Contains the nodes which are examined - timer = 0 - - while len(open_list) > 0: - if timer > 300: - return None, None - - current_node = min(open_list, key = lambda node: node.f) # The node with the smallest f value - open_list.remove(current_node) # Remove from open list - - if current_node.location == food_loc: # Found the food! Stop search - traceback = [food_loc] - pointer = current_node - while pointer.parent is not None: - traceback.append(pointer.parent.location) - pointer = pointer.parent - return traceback, current_node.g - - # Identify all possible nodes to move to - neighbour_nodes = [] - possible_moves = obvious_moves( - game_state, - my_head=current_node.location, - my_neck=current_node.parent.location if current_node.parent is not None else None, - risk_averse=True - ) - - for possible_move in possible_moves: - possible_node = Node(look_ahead(current_node.location, possible_move), parent=current_node) - neighbour_nodes.append(possible_node) - - for possible_node in neighbour_nodes: # For each possible new node - if possible_node.location in closed_list: # Skip if the node is in the closed list - continue - - # Compute f value for the neighbour node - possible_node.g = current_node.g + 1 - possible_node.h = manhattan_distance(current_node.location, food_loc) - possible_node.f = possible_node.g + possible_node.h - - # If the node's location is also in the open list but has a worse g value, ignore since we can do better - for open_node in open_list: - if possible_node.location == open_node.location and possible_node.g > open_node.g: - break - - # Otherwise, this is the best we can do so far - add to open list - open_list.append(possible_node) - - closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours - timer += 1 - - return None, None - -def flood_fill(game_state, next_square, risk_averse=True): - """ - Recursive function to get the total area of the current fill selection - """ - board_width = game_state['board']['width'] - board_height = game_state['board']['height'] - my_length = game_state["you"]["length"] - - # Make an (l x w) array and simulate the board - board = np.zeros((board_width, board_height)) - for snake in game_state["board"]["snakes"]: - body_pixels = snake["body"] - for pixel in body_pixels: - board[pixel["x"], pixel["y"]] = 1 - - if snake["id"] == game_state['you']["id"]: # Ignore yourself - continue - elif risk_averse and my_length < snake["length"]: - opponent_head = body_pixels[0] - opponent_moves = [look_ahead(opponent_head, direction) for direction in ["up", "down", "left", "right"]] - for m in opponent_moves: - if 0 <= m["x"] < board_width and 0 <= m["y"] < board_height: - board[m["x"], m["y"]] = 1 - - def fill(x, y): - if board[x][y] == 1: # Snakes or hazards - return - if board[x][y] == 2: # Fill - return - board[x][y] = 2 - neighbours = [(x-1,y),(x+1,y),(x,y-1),(x,y+1)] - for n in neighbours: - if 0 <= n[0] < board_width and 0 <= n[1] < board_height: - fill(n[0], n[1]) - - fill(next_square["x"], next_square["y"]) - return sum((row == 2).sum() for row in board) - - -def fill_search(game_state, my_head, safe_moves, log): - best_fill = -1 - next_move = safe_moves[0] - for safe_move in safe_moves: - end_square = look_ahead(my_head, safe_move) - max_fill = flood_fill(game_state, next_square=end_square) - if max_fill > best_fill: - next_move = safe_move - best_fill = max_fill - if log: - print(f"Flood fill on {safe_move} and got {max_fill}") - return next_move - -def order_food(game_state, head_loc): - """ - Return a sorted list of food options by distance to the snake. Also decide a cut-off based on health - """ - menu = game_state["board"]["food"] - health = game_state["you"]["health"] - # For each food item, grab the Manhattan distance to the snake - food_costs = [] - for food in menu: - cost_to_item = manhattan_distance(head_loc, food) - food_costs.append(cost_to_item) - - def helper_food_filter(menu, food_costs, cutoff): - menu_indices = np.where(np.array(food_costs) <= cutoff) - filt_menu = menu[menu_indices] - return - - # Sort the food by the distances and apply discretionary filters based on how hungry you are - sorted_menu = sorted(menu, key=lambda x: food_costs[menu.index(x)]) - sorted_food_costs = sorted(food_costs) - if len(menu) == 0: - new_menu = [] - elif health >= 45: # Big chilling - new_menu = [x for x in sorted_menu if sorted_food_costs[sorted_menu.index(x)] <= 5] - elif health <= min(food_costs) * 3: # Pretty desperate - new_menu = [x for x in sorted_menu if sorted_food_costs[sorted_menu.index(x)] <= 15] - else: - new_menu = [x for x in sorted_menu if sorted_food_costs[sorted_menu.index(x)] <= 10] - - return new_menu diff --git a/main_fr.py b/main_fr.py deleted file mode 100644 index 9ac285e0..00000000 --- a/main_fr.py +++ /dev/null @@ -1,85 +0,0 @@ -# Welcome to -# __________ __ __ .__ __ -# \______ \_____ _/ |__/ |_| | ____ ______ ____ _____ | | __ ____ -# | | _/\__ \\ __\ __\ | _/ __ \ / ___// \\__ \ | |/ // __ \ -# | | \ / __ \| | | | | |_\ ___/ \___ \| | \/ __ \| <\ ___/ -# |________/(______/__| |__| |____/\_____>______>___|__(______/__|__\\_____> -# - -import numpy as np -import random -import time -import typing - -from minimax import Battlesnake - - -global board_log - -def info() -> typing.Dict: - """ - info is called when you create your Battlesnake on play.battlesnake.com - and controls your Battlesnake's appearance - TIP: If you open your Battlesnake URL in a browser you should see this data - """ - print("INFO") - - return { - "apiversion": "1", - "author": "G", - "color": "#3333ff", - "head": "ski", - "tail": "mystic-moon", - } - - -def start(game_state: typing.Dict): - """start is called when your Battlesnake begins a game""" - print("GAME START") - global board_log - board_log = open("board_log.txt", "w") - - -def end(game_state: typing.Dict): - """end is called when your Battlesnake finishes a game""" - print("GAME OVER\n") - - -def move(game_state: typing.Dict, log=True, timer_log=True) -> typing.Dict: - """ - move is called on every turn and returns your next move - Valid moves are "up", "down", "left", or "right" - See https://docs.battlesnake.com/api/example-move for available data - """ - - game = Battlesnake(game_state) - - # Find the safe moves from current position - clock_in = time.time_ns() - optimal_move = game.minimax_move() - if timer_log: - print(f"Found optimal move with Minimax in {(time.time_ns() - clock_in) // 1000000} ms") - if log: - print(f"Safe moves: {optimal_move}") - - if optimal_move is None: - risky_move = game.minimax_move(risk_averse=False) - if risky_move is not None: - optimal_move = risky_move - else: - print(f"MOVE {game_state['turn']}: No safe moves detected! Moving down") - return {"move": "down"} - - print(f"MOVE {game_state['turn']}: {optimal_move}") - print("=" * 10) - board_log.write(f"MOVE {game_state['turn']}\n") - board_log.write(str(game_state)) - board_log.write("\n" + "=" * 100) - return {"move": optimal_move} - - -# Start server when `python main.py` is run -if __name__ == "__main__": - from server import run_server - - run_server({"info": info, "start": start, "move": move, "end": end}) diff --git a/minimax.py b/minimax.py deleted file mode 100644 index dd2713ab..00000000 --- a/minimax.py +++ /dev/null @@ -1,788 +0,0 @@ -import copy -import itertools -import logging -import numpy as np -import sys -import time -from collections import Counter - -logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) -logging.disable(logging.INFO) - -# tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} - - -class Battlesnake: - def __init__(self, game_state): - self.turn = game_state["turn"] - self.board_width = game_state["board"]["width"] - self.board_height = game_state["board"]["height"] - self.food = game_state["board"]["food"] - self.hazards = game_state["board"]["hazards"] - self.board = np.full((self.board_width, self.board_height), " ") - # Our snake's data - self.my_id = game_state["you"]["id"] - self.my_head = game_state["you"]["head"] - self.my_neck = game_state["you"]["body"][1] - self.my_body = game_state["you"]["body"] - self.my_length = game_state["you"]["length"] - self.my_health = game_state["you"]["health"] - # Read snake positions as a dictionary of dictionaries (easier to access than list of dicts) - self.all_snakes_dict = {} - for snake in game_state["board"]["snakes"]: - self.all_snakes_dict[snake["id"]] = { - "head": snake["head"], - "neck": snake["body"][1], - "body": snake["body"], - "length": snake["length"], - "health": snake["health"] - } - if "food_eaten" in snake.keys(): - self.all_snakes_dict[snake["id"]]["food_eaten"] = snake["food_eaten"] - if "name" in game_state["you"] and game_state["you"]["name"] != "Nightwing" and snake["name"] == "Nightwing": - self.my_id = snake["id"] - self.my_head = snake["head"] - self.my_neck = snake["body"][1] - self.my_body = snake["body"] - self.my_length = snake["length"] - self.my_health = snake["health"] - if self.my_id not in self.all_snakes_dict.keys(): - self.all_snakes_dict[self.my_id] = { - "head": self.my_head, - "neck": self.my_neck, - "body": self.my_body, - "length": self.my_length, - "health": self.my_health - } - # Opponent snakes - self.opponents = self.all_snakes_dict.copy() - self.opponents.pop(self.my_id) - - self.update_board() - self.minimax_search_depth = 4 - self.peripheral_dim = 3 - - def __copy__(self): - """Making a deep copy of the game_state dictionary takes too much time, so let's manually build it from - scratch. That way, we can modify a copied instance of the class without affecting the original instance.""" - all_snakes = [] - for snake_id, snake in self.all_snakes_dict.items(): - all_snakes.append({ - "id": snake_id, - "head": snake["head"], - "body": snake["body"].copy(), - "length": snake["length"], - "health": snake["health"] - }) - if "food_eaten" in snake.keys(): - all_snakes[-1]["food_eaten"] = snake["food_eaten"] - board = { - "height": self.board_height, - "width": self.board_width, - "food": self.food.copy(), - "hazards": self.hazards.copy(), - "snakes": all_snakes - } - you = { - "id": self.my_id, - "health": self.my_health, - "body": self.my_body.copy(), - "head": self.my_head, - "length": self.my_length - } - new_game_state = {"turn": self.turn, "board": board, "you": you} - return Battlesnake(new_game_state) - - def update_board(self): - """Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" - represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner""" - self.board = np.full((self.board_width, self.board_height), " ") - for num, my_square in enumerate(self.my_body): - self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "O" - for opponent in self.opponents.values(): - snake_body = opponent["body"] - for num, snake_sq in enumerate(snake_body): - self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "X" - - def display_board(self, board=None, return_string=False): - """Print out a nicely formatted board for convenient debugging""" - render_board = board if board is not None else self.board - for j in range(1, len(render_board[0]) + 1): - display_row = "" - for i in range(0, len(render_board)): - display_row += f"{render_board[i][-j]}| " - logging.info(display_row) - - if return_string: - board_str = "" - for j in range(1, len(render_board[0]) + 1): - display_row = "" - for i in range(0, len(render_board)): - display_row += f"{render_board[i][-j]}| " - board_str += display_row + "\n" - return board_str - - @staticmethod - def look_ahead(head, move): - """Given a possible move, return the snake's new coordinates if it were headed that way""" - snake_head = head.copy() - if move == "up": - snake_head["y"] += 1 - if move == "down": - snake_head["y"] -= 1 - if move == "left": - snake_head["x"] -= 1 - if move == "right": - snake_head["x"] += 1 - return snake_head - - @staticmethod - def manhattan_distance(start, end): - """Return the Manhattan distance for two positions""" - start = (start["x"], start["y"]) - end = (end["x"], end["y"]) - return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) - - def peripheral_vision(self, snake_id, direction): - """Return [x1, x2] and [y1, y2] of a portion of the board that functions as 'peripheral vision' for the snake""" - # Our peripheral field of vision when scanning for moves - head = self.all_snakes_dict[snake_id]["head"].copy() - neck = self.all_snakes_dict[snake_id]["neck"].copy() - dim = self.peripheral_dim - - if direction == "auto": # Gotta figure out the direction ourselves - if neck["x"] < head["x"]: - direction = "right" - elif neck["x"] > head["x"]: - direction = "left" - elif neck["y"] < head["y"]: - direction = "up" - elif neck["y"] > head["y"]: - direction = "down" - head = neck.copy() # Roll back our head location - - if direction == "right": # Neck is left of head, don't move left - peripheral_box_x = head["x"] + 1, min(head["x"] + dim + 1, self.board_width) - peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) - head["x"], head["y"] = 0, head["y"] - peripheral_box_y[0] - elif direction == "left": # Neck is right of head, don't move right - peripheral_box_x = max(head["x"] - dim, 0), head["x"] - peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) - head["x"], head["y"] = max(head["x"] - peripheral_box_x[0] - 1, 0), head["y"] - peripheral_box_y[0] - elif direction == "up": # Neck is below head, don't move down - peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) - peripheral_box_y = head["y"] + 1, min(head["y"] + dim + 1, self.board_height) - head["x"], head["y"] = head["x"] - peripheral_box_x[0], 0 - elif direction == "down": # Neck is above head, don't move up - peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) - peripheral_box_y = max(head["y"] - dim, 0), head["y"] - head["x"], head["y"] = head["x"] - peripheral_box_x[0], max(head["y"] - peripheral_box_y[0] - 1, 0) - else: # Centred around our snake's head - peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) - peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) - head["x"], head["y"] = head["x"] - peripheral_box_x[0], head["y"] - peripheral_box_y[0] - - return peripheral_box_x, peripheral_box_y, head - - def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, sort_by_peripheral=False): - """Return a list of valid moves for any hypothetical snake. If risk_averse is True, avoid any moves that may - potentially cause a collision (but only if the snake is shorter) - - Use cases: - - self.get_obvious_moves(snake_id) will return moves for any opponent snake for the current board - - self.get_obvious_moves(risk_averse=True) will return possible moves that avoid death-inducing collisions with - our snake. Set False to assume our opponents are out to get us (paranoid minimax) - """ - risky_moves = [] # Initialise a set of possible risky moves - possible_moves = {"up", "down", "left", "right"} # Initialise a set of possible moves - - head = self.all_snakes_dict[snake_id]["head"] - neck = self.all_snakes_dict[snake_id]["neck"] - length = self.all_snakes_dict[snake_id]["length"] - - clock_in = time.time_ns() - if neck["x"] < head["x"]: # Neck is left of head, don't move left - possible_moves.discard("left") - elif neck["x"] > head["x"]: # Neck is right of head, don't move right - possible_moves.discard("right") - elif neck["y"] < head["y"]: # Neck is below head, don't move down - possible_moves.discard("down") - elif neck["y"] > head["y"]: # Neck is above head, don't move up - possible_moves.discard("up") - - # Prevent snake from moving out of bounds - if head["x"] == 0: - possible_moves.discard("left") - if head["x"] + 1 == self.board_height: - possible_moves.discard("right") - if head["y"] == 0: - possible_moves.discard("down") - if head["y"] + 1 == self.board_width: - possible_moves.discard("up") - - # Prevent snake from colliding with other snakes - for opp_snake_id, opp_snake in self.all_snakes_dict.items(): - for move in possible_moves.copy(): # Remaining moves - possible_hit = self.look_ahead(head, move) - # If the potential move hits a snake's body, it's invalid (exclude the tail since it moves forward) - if snake_id == self.my_id and possible_hit in opp_snake["body"][:-1]: - possible_moves.discard(move) - elif snake_id != self.my_id and possible_hit != self.my_head and ( - (possible_hit in opp_snake["body"][:-1] and opp_snake_id != self.my_id) - or (possible_hit in opp_snake["body"] and opp_snake_id == self.my_id)): - possible_moves.discard(move) - # If the snake is risk-averse, avoid any chance of head-on collisions only if our snake is shorter - elif snake_id != opp_snake_id and length <= opp_snake["length"] \ - and self.manhattan_distance(possible_hit, opp_snake["head"]) <= 1: - if not (snake_id != self.my_id and opp_snake_id == self.my_id and length == opp_snake["length"]): # Allow opponents to be suicidal (so our snakes are the same size) - risky_moves.append(move) - if risk_averse: - possible_moves.discard(move) - - if sort_by_dist_to is not None: - head2 = self.all_snakes_dict[sort_by_dist_to]["head"] - possible_moves = sorted(possible_moves, # This converts a set to a list anyway - key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) - elif sort_by_peripheral: - possible_moves = sorted(possible_moves, # This converts a set to a list anyway - key=lambda move2: self.flood_fill(snake_id, confined_area=move2), - reverse=True) - else: - possible_moves = list(possible_moves) - - # De-prioritise any risky moves and send them to the back - if len(risky_moves) > 0: - for risky in risky_moves: - if risky in possible_moves: - possible_moves.append(possible_moves.pop(possible_moves.index(risky))) - - logging.info(f"Identified obvious moves {list(possible_moves)} in " - f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return possible_moves - - def minimax_move(self): - """Let's run the minimax algorithm with alpha-beta pruning!""" - # Compute the best score of each move using the minimax algorithm with alpha-beta pruning - if self.turn < 5: - search_depth = 1 - elif len(self.opponents) > 6: - search_depth = 2 # TODO should be risk-averse - elif len(self.opponents) >= 4: - search_depth = 4 - else: - search_depth = self.minimax_search_depth - _, best_move = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) - return best_move - - def is_game_over(self, for_snake_id=None, depth=None): - """Return True if the game ended for our snake, False otherwise. Use for_snake_id to return a boolean for a - specific snake's status (can be one or multiple).""" - snake_monitor = {} # A dictionary for each snake showing whether they're alive - for snake_id, snake in self.all_snakes_dict.items(): - snake_monitor[snake_id] = True - - head = snake["head"] - # Prevent snake from moving out of bounds - if head["x"] < 0 or head["x"] >= self.board_height: - snake_monitor[snake_id] = False - if head["y"] < 0 or head["y"] >= self.board_width: - snake_monitor[snake_id] = False - - # Skip this if we're at the beginning of the game when all snakes are still coiled up - if not (snake["length"] == 3 and snake["body"][-2] == snake["body"][-1]): - # Prevent snake from colliding with any other snakes - for opp_snake_id, opp_snake in self.all_snakes_dict.items(): - # Depending on the depth, exclude the tail if the turn technically isn't over (all players need to - # have made a move) - check_body = opp_snake["body"][1:-1] if depth % 2 == 1 else opp_snake["body"][1:] - - # If the potential move hits a snake's body, it's invalid - if head in check_body: - snake_monitor[snake_id] = False - # If the potential move hits a snake's head, it's invalid only if our snake is shorter - if snake_id != opp_snake_id: - if head == opp_snake["head"] and snake["length"] <= opp_snake["length"]: - snake_monitor[snake_id] = False - - # Game is over if there's only one snake remaining or if our snake died - game_over = True if (sum(snake_monitor.values()) == 1 or not snake_monitor[self.my_id]) else False - - # See if a specific snake is alive or not - if isinstance(for_snake_id, (list, tuple)): - snake_still_alive = [snake_monitor[snake_id] for snake_id in for_snake_id] - else: - snake_still_alive = snake_monitor[self.my_id] if for_snake_id is None else snake_monitor[for_snake_id] - - return game_over, snake_still_alive - - def heuristic(self, depth_number): - """Let's figure out a way to evaluate the current board for our snake :) - - Should be a function of: - - Maximising available space for our snake (flood fill) - - Minimising available space for enemy snakes - - Distance to food - - How far down our game tree we went in our minimax search - """ - # Determine how many layers deep in the game tree we are - layers_deep = self.minimax_search_depth - depth_number - - # If an opponent snake dies :) - opponents_left = len(self.opponents) - - # Determine available space via flood fill - available_space = self.flood_fill(self.my_id, risk_averse=True) - if available_space < 4: - space_penalty = -200 - else: - space_penalty = 0 - - # Estimate the space we have in our peripheral vision - available_peripheral = self.flood_fill(self.my_id, confined_area="auto") - - # # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in - # # our vicinity) - # if len(self.opponents) <= 4 \ - # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 4 \ - # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): - # self.peripheral_dim = 4 - # available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") - # else: - # available_enemy_space = 0 - - # Get closer to enemy snakes if we're longer by 3 - if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): - dist_to_enemy = self.dist_from_enemies()[0] - else: - dist_to_enemy = 0 - - # If we're getting too close to enemy snakes that are longer, RETREAT - threats = [self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] - num_threats = (np.count_nonzero(np.array(threats) <= 2) * 2 - + np.count_nonzero(np.array(threats) == 3) * 1) - - # Determine the closest safe distance to food - dist_food = self.dist_to_nearest_food() - health_flag = True if self.my_health < 40 else False - shortest_flag = True if sum([self.my_length <= snake["length"] for snake in self.opponents.values()]) >= min([2, len(self.opponents)]) else False - - - # Are we in the centre of the board? Maximise control - centre = range(self.board_width // 2 - 2, self.board_width // 2 + 3) - in_centre = (self.my_head["x"] in centre and self.my_head["x"] in centre) and (len(self.opponents) <= 2) - - # Heuristic formula - space_weight = 1 - peripheral_weight = 2 - enemy_left_weight = 1000 - enemy_restriction_weight = 75 if len(self.opponents) > 2 else 200 - food_weight = 250 if health_flag else 175 if shortest_flag else 75 - depth_weight = 25 - length_weight = 300 - centre_control_weight = 10 - aggression_weight = 250 if dist_to_enemy > 0 else 0 - threat_proximity_weight = -45 - - logging.info(f"Available space: {available_space}") - logging.info(f"Available peripheral: {available_peripheral}") - logging.info(f"Enemies left: {opponents_left}") - logging.info(f"Threats within 3 squares of us: {num_threats}") - logging.info(f"Distance to nearest enemy: {dist_to_enemy}") - logging.info(f"Distance to nearest food: {dist_food}") - logging.info(f"Layers deep in search tree: {layers_deep}") - # logging.info(f"Available enemy space: {available_enemy_space}") - logging.info(f"In centre: {in_centre}") - logging.info(f"Length: {self.my_length}") - - h = (available_space * space_weight) + space_penalty + \ - (peripheral_weight * available_peripheral) + \ - (enemy_left_weight / (opponents_left + 1)) + \ - (threat_proximity_weight * num_threats) + \ - (food_weight / (dist_food + 1)) + \ - (layers_deep * depth_weight) + \ - (self.my_length * length_weight) + \ - in_centre * centre_control_weight + \ - aggression_weight / (dist_to_enemy + 1) - # (enemy_restriction_weight / (available_enemy_space + 1)) + \ - - - return h - - def flood_fill(self, snake_id, confined_area=None, risk_averse=False): - """Recursive function to get the total area of the current fill selection. Basically, count how many £ symbols - we can fill while avoiding any $, O, and X symbols (obstacles). Confined_area tells the function to do flood - fill only on one side of the snake (should be either left/right/up/down).""" - head = self.all_snakes_dict[snake_id]["head"] - - # Assume we're doing flood fill for our snake - if snake_id == self.my_id: - board = copy.deepcopy(self.board) - # Let's try to avoid any squares that our enemy can go to - if risk_averse: - threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] - for threat in threats: - x, y = threat["x"], threat["y"] - avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in avoid_sq: - if not (n[0] == head["x"] and n[1] == head["y"]) and \ - (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): - board[n[0]][n[1]] = "X" - # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) - else: - board = np.full((self.board_width, self.board_height), " ") - for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): - board[square["x"], square["y"]] = "£" if num == 0 else "O" - for other_id, other_snake in self.all_snakes_dict.items(): - if other_id != snake_id: - for num, other_square in enumerate(other_snake["body"]): - board[other_square["x"], other_square["y"]] = "$" if num == 0 else "X" - - if confined_area is not None: - xs, ys, head = self.peripheral_vision(snake_id, confined_area) - board = board[xs[0]:xs[1], ys[0]:ys[1]] - # if -1 == head["x"]: - # self.peripheral_vision(snake_id, confined_area) - - def fill(x, y, board, initial_square): - if board[x][y] in ["X", "$"]: # Snakes or hazards - return - if board[x][y] in ["O"]: # Us - return - if board[x][y] == "£" and not initial_square: # Already filled - return - board[x][y] = "£" - neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in neighbours: - if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): - fill(n[0], n[1], board, initial_square=False) - - fill(head["x"], head["y"], board, initial_square=True) - filled = sum((row == "£").sum() for row in board) - return filled - 1 if filled > 0 else filled # Exclude the head, but cannot ever be negative - - def dist_to_nearest_food(self): - """Return the Manhattan distance to the nearest food for our snake""" - best_dist = np.inf - for food in self.food: - dist = self.manhattan_distance(food, self.my_head) - dist_enemy = min([self.manhattan_distance(food, snake["head"]) for snake in self.opponents.values()]) - if dist < best_dist and ((dist_enemy >= 4 and dist <= dist_enemy * 2) or (4 > dist_enemy >= dist)): - best_dist = dist - return best_dist - - def dist_from_enemies(self): - """To gauge how in danger we are, return a list of Manhattan distances to all snakes""" - return sorted([self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values()]) - - def closest_enemy(self): - """Return the nearest enemy snake""" - return sorted(self.opponents.keys(), - key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]))[0] - - def simulate_move(self, move, snake_id, evaluate_deaths=False): - """Create a new Battlesnake instance with a simulated move for a given snake""" - clock_in = time.time_ns() - new_game = self.__copy__() - - # Simulate the new head position - old_head = self.all_snakes_dict[snake_id]["head"] - new_head = self.look_ahead(old_head, move) - - # Insert the simulated snake position into the new instance - new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] - new_game.all_snakes_dict[snake_id]["head"] = new_head - new_game.all_snakes_dict[snake_id]["health"] -= 1 - # Repeat for our snake's specific attributes - if snake_id == self.my_id: - new_game.my_body = [new_head] + new_game.my_body[:-1] - new_game.my_head = new_head - new_game.my_health -= 1 - - # If food was consumed, this elongates the snake from the tail and restores health - if new_head in self.food: - new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head - - # Check if any snakes died from this simulated move and remove them from the game - if evaluate_deaths: - # Update snake lengths from any food eaten - for update_id, snake in new_game.all_snakes_dict.items(): - if "food_eaten" in snake.keys(): - new_game.all_snakes_dict[update_id]["length"] += 1 - new_game.all_snakes_dict[update_id]["health"] = 100 - new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] - new_game.food = [food for food in self.food - if not (food["x"] == snake["food_eaten"]["x"] - and food["y"] == snake["food_eaten"]["y"])] - if update_id == self.my_id: - new_game.my_length += 1 - new_game.my_health = 100 - new_game.my_body += [new_game.my_body[-1]] - - # Reset the food tracker - del new_game.all_snakes_dict[update_id]["food_eaten"] - - all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] - count_heads = Counter(all_heads) - butt_heads = [k for k, v in count_heads.items() if v > 1] - for butt_head in butt_heads: - overlapping_snakes = np.array([ - (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() - if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) - ]) - lengths = overlapping_snakes[:, 1].astype(int) - # Special cases where the snake committed suicide and also killed our snake => don't remove - if not (self.my_id in overlapping_snakes[:, 0] - and np.count_nonzero(lengths == self.my_length) > 1): - indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() - if len(indices_largest_snakes) > 1: - winner_id = None - else: - winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] - - for rm_id in overlapping_snakes[:, 0]: - if rm_id != winner_id: - new_game.all_snakes_dict.pop(rm_id) - - new_game.update_board() - # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return new_game - - def minimax(self, depth, alpha, beta, maximising_snake, TEMP_TAG=False): - """Implement the minimax algorithm with alpha-beta pruning - - :param depth: - :param alpha: - :param beta: - :param maximising_snake: - :return: - """ - if depth != self.minimax_search_depth: - # Check if our snake died - game_over, still_alive = self.is_game_over(for_snake_id=self.my_id, depth=depth) - if not still_alive: - logging.info("Our snake died...") - return -1e6 + (self.minimax_search_depth - depth), None # Reward slower deaths - # Otherwise, if our snake is ALIVE and is the winner :) - elif game_over: - logging.info("OUR SNAKE WON") - return 1e6 + depth, None # Reward faster kills - - # At the bottom of the decision tree or if we won/lost the game - if depth == 0: - logging.info("=" * 50) - logging.info(f"DEPTH = {depth}") - heuristic = self.heuristic(depth_number=depth) - logging.info(f"Heuristic = {heuristic} at terminal node") - return heuristic, None - - # Our snake's turn - if maximising_snake: - logging.info("=" * 50) - logging.info(f"DEPTH = {depth} OUR SNAKE") - logging.info(f"ALPHA = {alpha} | beta = {beta}") - - clock_in = time.time_ns() - possible_moves = self.get_obvious_moves( # If > 6 opponents, we'll do depth = 2 and risk_averse = True - self.my_id, risk_averse=(len(self.opponents) > 6), sort_by_peripheral=True) - if len(possible_moves) == 0 and len(self.opponents) > 6: # Try again, but do any risky move - possible_moves = self.get_obvious_moves(self.my_id, risk_averse=False, sort_by_peripheral=True) - if len(possible_moves) == 0: # RIP - possible_moves = ["down"] - - best_val, best_move = -np.inf, None - for num, move in enumerate(possible_moves): - SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) - - logging.info(f"{len(possible_moves)} CHILD NODES: VISITING {num + 1} OF {len(possible_moves)}") - logging.info(f"Running minimax for OUR SNAKE moving {move}") - SIMULATED_BOARD_INSTANCE.display_board() - - # board_string = SIMULATED_BOARD_INSTANCE.display_board(return_string=True) - # global tree_tracker - # tree_tracker[depth].append(board_string) - - clock_in2 = time.time_ns() - node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, False) - - logging.info("=" * 50) - logging.info(f"BACK AT DEPTH = {depth} OUR SNAKE") - logging.info(f"ALPHA = {alpha} | beta = {beta}") - - # Update best score and best move - if np.argmax([best_val, node_val]) == 1: - best_move = move - best_val = max(best_val, node_val) - old_alpha = alpha - alpha = max(alpha, best_val) - - logging.info(f"Updated ALPHA from {old_alpha} to {alpha}") - logging.info(f"Identified best move so far = {best_move} in {round((time.time_ns() - clock_in2) / 1000000, 3)} ms") - - # Check to see if we can prune - if alpha >= beta: - logging.info(f"PRUNED!!! Alpha = {alpha} >= Beta = {beta}") - break - - logging.info(f"FINISHED MINIMAX LAYER on our snake in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return best_val, best_move - - # Opponents' turns - else: - logging.info("=" * 50) - logging.info(f"DEPTH = {depth} OPPONENT SNAKES") - logging.info(f"BETA = {beta} | alpha = {alpha}") - - clock_in = time.time_ns() - # If there are multiple opponent snakes, search only for those within a reasonable distance of ours - if len(self.opponents) == 1: - search_within = self.board_width * self.board_height - elif len(self.opponents) <= 3: - search_within = self.board_width - elif len(self.opponents) < 6: - search_within = self.board_width // 2 - else: - search_within = self.board_width // 2 - 1 - - # Grab possible moves for all opponent snakes - opps_nearby = 0 # Counter for opponents in our vicinity - opps_moves = {} # Store possible moves for each snake id - for opp_id, opp_snake in self.opponents.items(): - opp_move = self.get_obvious_moves(opp_id, risk_averse=False, sort_by_dist_to=self.my_id) - if len(opp_move) == 0: # If the snake has no legal moves, move down and die - opp_move = ["down"] - - # Save time by only searching for snakes within close range - if self.manhattan_distance(self.my_head, opp_snake["head"]) < search_within: - opps_moves[opp_id] = opp_move # ALL OF THEM - opps_nearby += 1 - else: - if len(self.opponents) <= 5: - opps_moves[opp_id] = [opp_move[0]] - - logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " - f"squares of us in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") - - clock_in = time.time_ns() - # If >= 3 board simulations, then randomly sample 3 of them based on how threatening the position is to our - # snake to cut down on runtime - all_opp_combos = list(itertools.product(*opps_moves.values())) - if len(all_opp_combos) > 2 and len(self.opponents) > 2: - logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") - all_opp_combos = all_opp_combos[:2] - elif len(all_opp_combos) > 3: - logging.info(f"FOUND {len(all_opp_combos)} BOARDS BUT CUTTING DOWN TO 2") - all_opp_combos = all_opp_combos[:3] - - possible_movesets = [] - possible_sims = [] - # Get all possible boards by simulating moves for each opponent snake, one at a time - for move_combo in all_opp_combos: - SIMULATED_BOARD_INSTANCE = self.__copy__() - for num, move in enumerate(move_combo): - evaluate_flag = (num + 1 == len(move_combo)) - SIMULATED_BOARD_INSTANCE = SIMULATED_BOARD_INSTANCE.simulate_move( - move, list(opps_moves.keys())[num], evaluate_deaths=evaluate_flag) - # SIMULATED_BOARD_INSTANCE.display_board() - possible_sims.append(SIMULATED_BOARD_INSTANCE.__copy__()) - possible_movesets.append(move_combo) - - logging.info(f"SIMULATED {len(possible_sims)} POSSIBLE BOARDS OF OPPONENT MOVE COMBOS in " - f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") - - clock_in = time.time_ns() - best_val, best_move = np.inf, None - for num, SIMULATED_BOARD_INSTANCE in enumerate(possible_sims): - logging.info(f"{len(possible_sims)} CHILD NODES: VISITING {num + 1} OF {len(possible_sims)}") - logging.info(f"Running minimax for OPPONENT SNAKES moving {possible_movesets[num]}") - SIMULATED_BOARD_INSTANCE.display_board() - clock_in2 = time.time_ns() - node_val, node_move = SIMULATED_BOARD_INSTANCE.minimax(depth - 1, alpha, beta, True, TEMP_TAG=True) - - logging.info("=" * 50) - logging.info(f"BACK AT DEPTH = {depth} OPPONENT SNAKES") - logging.info(f"BETA = {beta} | alpha = {alpha}") - - # Update best score and best move - if np.argmin([best_val, node_val]) == 1: - best_move = possible_movesets[num] - best_val = min(best_val, node_val) - old_beta = beta - beta = min(beta, best_val) - - logging.info(f"Updated BETA from {old_beta} to {beta}") - logging.info(f"Identified best move so far = {best_move} in {round((time.time_ns() - clock_in2) / 1000000, 3)} ms") - - # Check to see if we can prune - if beta <= alpha: - logging.info(f"PRUNED!!! Beta = {beta} <= Alpha = {alpha}") - break - - logging.info(f"FINISHED MINIMAX LAYER on opponents in {(time.time_ns() - clock_in) // 1000000} ms") - return best_val, best_move - -# def a_star_search(self, food_loc): -# """ -# Implement the A* search algorithm to find the shortest path to food -# """ -# head_loc = self.my_head.copy() -# open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet -# closed_list = [] # Contains the nodes which are examined -# timer = 0 -# -# while len(open_list) > 0: -# if timer > 300: -# return None, None -# -# current_node = min(open_list, key=lambda node: node.f) # The node with the smallest f value -# open_list.remove(current_node) # Remove from open list -# -# if current_node.location == food_loc: # Found the food! Stop search -# traceback = [food_loc] -# pointer = current_node -# while pointer.parent is not None: -# traceback.append(pointer.parent.location) -# pointer = pointer.parent -# return traceback, current_node.g -# -# # Identify all possible nodes to move to -# neighbour_nodes = [] -# possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) -# -# for possible_move in possible_moves: -# possible_node = Node(self.look_ahead(current_node.location, possible_move), parent=current_node) -# neighbour_nodes.append(possible_node) -# -# for possible_node in neighbour_nodes: # For each possible new node -# if possible_node.location in closed_list: # Skip if the node is in the closed list -# continue -# -# # Compute f value for the neighbour node -# possible_node.g = current_node.g + 1 -# possible_node.h = self.manhattan_distance(current_node.location, food_loc) -# possible_node.f = possible_node.g + possible_node.h -# -# # If the node's location is also in the open list but has a worse g value, ignore since we can do better -# for open_node in open_list: -# if possible_node.location == open_node.location and possible_node.g > open_node.g: -# break -# -# # Otherwise, this is the best we can do so far - add to open list -# open_list.append(possible_node) -# -# closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours -# timer += 1 -# -# return None, None -# -# -class Node: - def __init__(self, location, parent=None, f=0, g=0, h=0): - self.location = location - self.neighbour_nodes = [] - self.parent = parent - self.f = f # The cost from the start node to the goal node - self.g = g # The cost from the start node to the current node - self.h = h # The cost from the current node to the goal node using a heuristic - - def __repr__(self): - return str(self.location) \ No newline at end of file diff --git a/tests.py b/tests.py new file mode 100644 index 00000000..ab0b34cd --- /dev/null +++ b/tests.py @@ -0,0 +1,24 @@ +from snake_engine import Battlesnake + + +# Should've gotten food +game_state = {"game":{"id":"8533c7c3-8372-4d85-acc4-4954d5c0aa4b","ruleset":{"name":"standard","version":"cli","settings":{"foodSpawnChance":15,"minimumFood":1,"hazardDamagePerTurn":14,"hazardMap":"","hazardMapAuthor":"","royale":{"shrinkEveryNTurns":25},"squad":{"allowBodyCollisions":False,"sharedElimination":False,"sharedHealth":False,"sharedLength":False}}},"map":"standard","timeout":500,"source":""},"turn":178,"board":{"height":11,"width":11,"snakes":[{"id":"30b31eb5-c9cf-48e7-9c40-b3824fff2cc3","name":"Nightwing","latency":"351","health":100,"body":[{"x":6,"y":8},{"x":5,"y":8},{"x":4,"y":8},{"x":3,"y":8},{"x":3,"y":7},{"x":2,"y":7},{"x":1,"y":7},{"x":0,"y":7},{"x":0,"y":6},{"x":0,"y":5},{"x":0,"y":4},{"x":0,"y":3},{"x":0,"y":2},{"x":1,"y":2},{"x":1,"y":3},{"x":1,"y":3}],"head":{"x":6,"y":8},"length":16,"shout":"","squad":"","customizations":{"color":"#3333ff","head":"ski","tail":"mystic-moon"}},{"id":"46b6ee1d-6ed1-472b-ab03-53fff9c83431","name":"JonK","latency":"23","health":96,"body":[{"x":9,"y":3},{"x":9,"y":2},{"x":9,"y":1},{"x":10,"y":1},{"x":10,"y":0},{"x":9,"y":0},{"x":8,"y":0},{"x":7,"y":0},{"x":6,"y":0},{"x":5,"y":0},{"x":4,"y":0},{"x":4,"y":1},{"x":4,"y":2},{"x":4,"y":3},{"x":4,"y":4},{"x":5,"y":4},{"x":6,"y":4},{"x":7,"y":4}],"head":{"x":9,"y":3},"length":18,"shout":"","squad":"","customizations":{"color":"#B7410E","head":"sleepy","tail":"offroad"}}],"food":[{"x":9,"y":10},{"x":10,"y":9}],"hazards":[]},"you":{"id":"ad40b1ef-e988-45ab-a06d-ad3f0e060f91","name":"Glynn","latency":"135","health":88,"body":[{"x":6,"y":2},{"x":5,"y":2},{"x":4,"y":2}],"head":{"x":6,"y":2},"length":3,"shout":"","squad":"","customizations":{"color":"#6600ff","head":"all-seeing","tail":"weight"}}} +next_move = Battlesnake(game_state).optimal_move() +assert next_move in ["up", "right"] + +# Avoid getting trapped +game_state = {"game":{"id":"8533c7c3-8372-4d85-acc4-4954d5c0aa4b","ruleset":{"name":"standard","version":"cli","settings":{"foodSpawnChance":15,"minimumFood":1,"hazardDamagePerTurn":14,"hazardMap":"","hazardMapAuthor":"","royale":{"shrinkEveryNTurns":25},"squad":{"allowBodyCollisions":False,"sharedElimination":False,"sharedHealth":False,"sharedLength":False}}},"map":"standard","timeout":500,"source":""},"turn":246,"board":{"height":11,"width":11,"snakes":[{"id":"30b31eb5-c9cf-48e7-9c40-b3824fff2cc3","name":"Nightwing","latency":"255","health":89,"body":[{"x":10,"y":4},{"x":9,"y":4},{"x":9,"y":3},{"x":9,"y":2},{"x":9,"y":1},{"x":9,"y":0},{"x":8,"y":0},{"x":8,"y":1},{"x":8,"y":2},{"x":8,"y":3},{"x":8,"y":4},{"x":8,"y":5},{"x":7,"y":5},{"x":7,"y":4},{"x":7,"y":3},{"x":7,"y":2},{"x":7,"y":1}],"head":{"x":10,"y":4},"length":17,"shout":"","squad":"","customizations":{"color":"#3333ff","head":"ski","tail":"mystic-moon"}},{"id":"46b6ee1d-6ed1-472b-ab03-53fff9c83431","name":"JonK","latency":"24","health":100,"body":[{"x":9,"y":7},{"x":8,"y":7},{"x":7,"y":7},{"x":6,"y":7},{"x":6,"y":8},{"x":5,"y":8},{"x":5,"y":9},{"x":5,"y":10},{"x":4,"y":10},{"x":3,"y":10},{"x":2,"y":10},{"x":2,"y":9},{"x":1,"y":9},{"x":0,"y":9},{"x":0,"y":8},{"x":0,"y":7},{"x":0,"y":6},{"x":0,"y":5},{"x":0,"y":4},{"x":1,"y":4},{"x":2,"y":4},{"x":2,"y":5},{"x":2,"y":6},{"x":1,"y":6},{"x":1,"y":7},{"x":1,"y":7}],"head":{"x":9,"y":7},"length":26,"shout":"","squad":"","customizations":{"color":"#B7410E","head":"sleepy","tail":"offroad"}}],"food":[{"x":5,"y":5}],"hazards":[]},"you":{"id":"ad40b1ef-e988-45ab-a06d-ad3f0e060f91","name":"Glynn","latency":"135","health":88,"body":[{"x":6,"y":2},{"x":5,"y":2},{"x":4,"y":2}],"head":{"x":6,"y":2},"length":3,"shout":"","squad":"","customizations":{"color":"#6600ff","head":"all-seeing","tail":"weight"}}} +game_state = {"game":{"id":"93583e37-a5d1-4f1a-b682-57883240be6a","ruleset":{"name":"standard","version":"cli","settings":{"foodSpawnChance":15,"minimumFood":1,"hazardDamagePerTurn":14,"hazardMap":"","hazardMapAuthor":"","royale":{"shrinkEveryNTurns":25},"squad":{"allowBodyCollisions":False,"sharedElimination":False,"sharedHealth":False,"sharedLength":False}}},"map":"standard","timeout":500,"source":""},"turn":219,"board":{"height":11,"width":11,"snakes":[{"id":"3d44d587-99e5-4340-8ae4-143247074998","name":"JonK","latency":"21","health":98,"body":[{"x":9,"y":6},{"x":9,"y":5},{"x":10,"y":5},{"x":10,"y":4},{"x":9,"y":4},{"x":8,"y":4},{"x":7,"y":4},{"x":7,"y":3},{"x":6,"y":3},{"x":5,"y":3},{"x":5,"y":2},{"x":4,"y":2},{"x":3,"y":2},{"x":3,"y":3},{"x":4,"y":3},{"x":4,"y":4},{"x":5,"y":4},{"x":5,"y":5},{"x":6,"y":5},{"x":7,"y":5}],"head":{"x":9,"y":6},"length":20,"shout":"","squad":"","customizations":{"color":"#B7410E","head":"sleepy","tail":"offroad"}},{"id":"b4346106-cf74-4809-ab5d-ee2d1fc4686f","name":"Nightwing","latency":"39","health":100,"body":[{"x":10,"y":1},{"x":9,"y":1},{"x":8,"y":1},{"x":7,"y":1},{"x":6,"y":1},{"x":5,"y":1},{"x":4,"y":1},{"x":3,"y":1},{"x":2,"y":1},{"x":1,"y":1},{"x":1,"y":2},{"x":0,"y":2},{"x":0,"y":3},{"x":1,"y":3},{"x":1,"y":4},{"x":1,"y":5},{"x":2,"y":5},{"x":2,"y":6},{"x":3,"y":6},{"x":3,"y":7},{"x":3,"y":7}],"head":{"x":10,"y":1},"length":21,"shout":"","squad":"","customizations":{"color":"#3333ff","head":"ski","tail":"mystic-moon"}}],"food":[{"x":5,"y":9}],"hazards":[]},"you":{"id":"362c38e0-ccb9-43f0-8190-fdae1b1ff8a6","name":"JonK3","latency":"32","health":48,"body":[{"x":8,"y":6},{"x":9,"y":6},{"x":9,"y":7},{"x":10,"y":7},{"x":10,"y":6}],"head":{"x":8,"y":6},"length":5,"shout":"","squad":"","customizations":{"color":"#B7410E","head":"sleepy","tail":"offroad"}}} +next_move = Battlesnake(game_state).optimal_move() +assert next_move in ["down"] + + +# More space +game_state = {"game":{"id":"b71bf7c8-36a9-47ca-b269-f512597a2527","ruleset":{"name":"standard","version":"cli","settings":{"foodSpawnChance":15,"minimumFood":1,"hazardDamagePerTurn":14,"hazardMap":"","hazardMapAuthor":"","royale":{"shrinkEveryNTurns":25},"squad":{"allowBodyCollisions":False,"sharedElimination":False,"sharedHealth":False,"sharedLength":False}}},"map":"standard","timeout":500,"source":""},"turn":28,"board":{"height":11,"width":11,"snakes":[{"id":"e82444ff-0970-4b12-b65f-a6336f5a32cc","name":"Rick2","latency":"137","health":98,"body":[{"x":5,"y":7},{"x":5,"y":6},{"x":4,"y":6},{"x":4,"y":5},{"x":4,"y":4},{"x":4,"y":3}],"head":{"x":5,"y":7},"length":6,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}},{"id":"bd22d84f-5c11-4cb2-96e9-5867ec8b59db","name":"Glynn","latency":"44","health":72,"body":[{"x":7,"y":7},{"x":8,"y":7},{"x":8,"y":8}],"head":{"x":7,"y":7},"length":3,"shout":"","squad":"","customizations":{"color":"#6600ff","head":"all-seeing","tail":"weight"}},{"id":"8cb70727-46d9-4b4c-a5be-d5b2a4bf753e","name":"Jesse","latency":"25","health":78,"body":[{"x":6,"y":8},{"x":5,"y":8},{"x":5,"y":9},{"x":6,"y":9}],"head":{"x":6,"y":8},"length":4,"shout":"","squad":"","customizations":{"color":"#E04C07","head":"missile","tail":"nr-booster"}},{"id":"091cc519-8e82-4b29-bc00-1d08b0c5489e","name":"Nightwing","latency":"103","health":96,"body":[{"x":0,"y":8},{"x":1,"y":8},{"x":1,"y":9},{"x":2,"y":9},{"x":2,"y":8},{"x":3,"y":8},{"x":3,"y":7},{"x":3,"y":6}],"head":{"x":0,"y":8},"length":8,"shout":"","squad":"","customizations":{"color":"#3333ff","head":"ski","tail":"mystic-moon"}},{"id":"478b4690-3f18-4846-af95-e0d96b894b32","name":"Rick","latency":"135","health":92,"body":[{"x":2,"y":4},{"x":2,"y":3},{"x":2,"y":2},{"x":2,"y":1},{"x":2,"y":0},{"x":3,"y":0},{"x":4,"y":0},{"x":5,"y":0}],"head":{"x":2,"y":4},"length":8,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}}],"food":[{"x":4,"y":10},{"x":5,"y":10}],"hazards":[]},"you":{"id":"e9c795aa-9235-4b12-9325-a449cc75b19f","name":"Matt2","latency":"258","health":90,"body":[{"x":10,"y":8},{"x":9,"y":8},{"x":8,"y":8}],"head":{"x":10,"y":8},"length":3,"shout":"","squad":"","customizations":{"color":"#1f9490","head":"default","tail":"default"}}} +next_move = Battlesnake(game_state).optimal_move() +assert next_move in ["up"] + +# don't need food +game_state={"game":{"id":"05c3c37b-4861-476a-bbc2-b0ce4d30a8af","ruleset":{"name":"standard","version":"cli","settings":{"foodSpawnChance":15,"minimumFood":1,"hazardDamagePerTurn":14,"hazardMap":"","hazardMapAuthor":"","royale":{"shrinkEveryNTurns":25},"squad":{"allowBodyCollisions":False,"sharedElimination":False,"sharedHealth":False,"sharedLength":False}}},"map":"standard","timeout":500,"source":""},"turn":45,"board":{"height":11,"width":11,"snakes":[{"id":"8e6c3427-ebf7-49ed-9b23-700342aef7f6","name":"JonK3","latency":"26","health":90,"body":[{"x":8,"y":3},{"x":7,"y":3},{"x":7,"y":4},{"x":7,"y":5},{"x":7,"y":6},{"x":7,"y":7},{"x":7,"y":8}],"head":{"x":8,"y":3},"length":7,"shout":"","squad":"","customizations":{"color":"#B7410E","head":"sleepy","tail":"offroad"}},{"id":"2d41193c-8534-400f-9717-d4ee9272ea4f","name":"Rick4","latency":"158","health":87,"body":[{"x":9,"y":2},{"x":8,"y":2},{"x":7,"y":2},{"x":7,"y":1},{"x":6,"y":1}],"head":{"x":9,"y":2},"length":5,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}},{"id":"9110ff66-e559-46ce-9a35-63628ba2ac2b","name":"Nightwing","latency":"72","health":99,"body":[{"x":1,"y":2},{"x":1,"y":3},{"x":0,"y":3},{"x":0,"y":4},{"x":0,"y":5},{"x":0,"y":6},{"x":0,"y":7},{"x":1,"y":7},{"x":1,"y":8}],"head":{"x":1,"y":2},"length":9,"shout":"","squad":"","customizations":{"color":"#3333ff","head":"ski","tail":"mystic-moon"}},{"id":"571a52e2-2e02-4b77-91f2-e8ad7af0c2f1","name":"Rick","latency":"151","health":78,"body":[{"x":3,"y":4},{"x":3,"y":5},{"x":3,"y":6},{"x":3,"y":7},{"x":3,"y":8},{"x":4,"y":8}],"head":{"x":3,"y":4},"length":6,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}},{"id":"4e3cc686-d592-40c1-bd25-3be41bccd872","name":"Rick2","latency":"114","health":94,"body":[{"x":6,"y":3},{"x":6,"y":4},{"x":6,"y":5},{"x":6,"y":6},{"x":5,"y":6},{"x":4,"y":6},{"x":4,"y":5},{"x":4,"y":4}],"head":{"x":6,"y":3},"length":8,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}}],"food":[{"x":0,"y":0}],"hazards":[]},"you":{"id":"571a52e2-2e02-4b77-91f2-e8ad7af0c2f1","name":"Rick","latency":"151","health":78,"body":[{"x":3,"y":4},{"x":3,"y":5},{"x":3,"y":6},{"x":3,"y":7},{"x":3,"y":8},{"x":4,"y":8}],"head":{"x":3,"y":4},"length":6,"shout":"","squad":"","customizations":{"color":"#00ff00","head":"caffeine","tail":"coffee"}}} +next_move = Battlesnake(game_state).optimal_move() +assert next_move in ["right"] \ No newline at end of file From 6562a4530d79290b988017cdf90722ef7d730b67 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 24 Oct 2023 22:03:06 +0100 Subject: [PATCH 38/64] Hm new revamp --- snake_engine.py | 468 ++++++++++++++++++++++++++++++------------------ 1 file changed, 296 insertions(+), 172 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 9d35fac9..3efe1a2a 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -1,3 +1,4 @@ +from __future__ import annotations import copy import itertools import logging @@ -5,9 +6,12 @@ import sys import time from collections import Counter -from networkx_tree import hierarchy_pos from matplotlib import colors, image as mpimg, pyplot as plt +from networkx_tree import hierarchy_pos +from typing import Optional +my_name = "Nightwing" +# Use these global variables to add data for visualising the minimax decision tree tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} tree_edges = [] tree_nodes = [] @@ -15,7 +19,13 @@ class Battlesnake: - def __init__(self, game_state, debugging=False): + def __init__(self, game_state: dict, debugging: Optional[bool] = False): + """ + Represents our Battlesnake in any given game state and includes all our decision-making methods + + :param game_state: The move API request (https://docs.battlesnake.com/api/example-move#move-api-response) + :param debugging: Set to True if you want to view a log of what's happening behind the minimax algorithm + """ # General game data self.turn = game_state["turn"] self.board_width = game_state["board"]["width"] @@ -33,7 +43,7 @@ def __init__(self, game_state, debugging=False): self.my_health = game_state["you"]["health"] # Read snake positions as a dictionary of dictionaries (easier to access than list of dicts) - self.all_snakes_dict = {} + self.all_snakes_dict: dict[str, dict] = {} for snake in game_state["board"]["snakes"]: self.all_snakes_dict[snake["id"]] = { "head": snake["head"], @@ -44,8 +54,7 @@ def __init__(self, game_state, debugging=False): "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else [] } # Weird cases when running locally where the "you" snake is not our actual snake or is empty - if ("name" in game_state["you"] and game_state["you"]["name"] != "Nightwing" - and snake["name"] == "Nightwing"): + if "name" in game_state["you"] and game_state["you"]["name"] != my_name and snake["name"] == my_name: self.my_id = snake["id"] self.my_head = snake["head"] self.my_neck = snake["body"][1] @@ -68,16 +77,20 @@ def __init__(self, game_state, debugging=False): # Finish up our constructor self.update_board() - self.minimax_search_depth = 4 - self.peripheral_dim = 3 + self.minimax_search_depth = 4 # Depth for minimax algorithm + self.peripheral_size = 3 # Length of our snake's "peripheral vision" self.debugging = debugging logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) if not self.debugging: logging.disable(logging.INFO) - def __copy__(self): - """Making a deep copy of the game_state dictionary takes too much time, so let's manually build it from - scratch. That way, we can modify a copied instance of the class without affecting the original instance.""" + def __copy__(self) -> Battlesnake: + """ + Making a deep copy of the game_state dictionary takes too much time, so let's manually build it from + scratch. That way, we can modify a copied instance of the class without affecting the original instance. + + :return: A new instance of our Battlesnake class + """ all_snakes = [] for snake_id, snake in self.all_snakes_dict.items(): all_snakes.append({ @@ -106,18 +119,38 @@ def __copy__(self): return Battlesnake(new_game_state, debugging=self.debugging) def update_board(self): - """Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" - represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner""" + """ + Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" + represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner. + """ self.board = np.full((self.board_width, self.board_height), " ") for num, my_square in enumerate(self.my_body): - self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "O" + self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" for opponent in self.opponents.values(): snake_body = opponent["body"] for num, snake_sq in enumerate(snake_body): - self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "X" + self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "x" - def display_board(self, board=None, return_string=False): - """Print out a nicely formatted board for convenient debugging""" + def display_board(self, board: Optional[np.array] = None, return_string: Optional[bool] = False): + """ + Print out a nicely formatted board for convenient debugging e.g. + + | | | | | | | | | | | + | | | | | | | | | | | + | | | | | o| o| o| o| o| o| + | | | | | o| o| | | | o| + x| x| | | | | | | | | o| + x| | | | | | | | | | o| + x| | | | | | | | | | o| + x| | | | £| o| o| o| o| o| o| + x| | | $| x| x| x| | | | | + x| x| x| x| x| x| x| | | | | + | | | | | | | | | | | + + :param board: Calling display_board() will print out the current board, but for debugging purposes, you can feed + in a different board variable to display + :param return_string: You can optionally choose to return the board as a string for you to print later + """ render_board = board if board is not None else self.board for j in range(1, len(render_board[0]) + 1): display_row = "" @@ -125,19 +158,29 @@ def display_board(self, board=None, return_string=False): display_row += f"{render_board[i][-j]}| " logging.info(display_row) - # Do you want to return the board as a string instead of printing it out? + # Return the board as a string instead of printing it out if return_string: board_str = "" for j in range(1, len(render_board[0]) + 1): display_row = "" for i in range(0, len(render_board)): - display_row += f"{render_board[i][-j]}| " + if render_board[i][-j] == " ": # Adjust for difference in sizes between spaces and x/o's + display_row += f" | " + else: + display_row += f"{render_board[i][-j]}| " board_str += display_row + "\n" return board_str @staticmethod - def look_ahead(head, move): - """Given a possible move, return the snake's new coordinates if it were headed that way""" + def look_ahead(head: dict, move: str) -> dict: + """ + Given a possible move, return the snake's new coordinates if it were headed that way + + :param head: The location of the snake's head as a dictionary e.g. {"x": 5, "y": 10} + :param move: Either "left", "right", "up", or "down" + + :return: The new location of the snake's head if it were to move in said direction e.g. {"x": 4, "y": 10} + """ snake_head = head.copy() if move == "up": snake_head["y"] += 1 @@ -150,15 +193,29 @@ def look_ahead(head, move): return snake_head @staticmethod - def manhattan_distance(start, end): - """Return the Manhattan distance for two positions""" + def manhattan_distance(start: dict, end: dict) -> int: + """ + Return the Manhattan distance for two positions + + :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} + :param end: A different location on the board + + :return: The Manhattan distance between the start and end inputs + """ start = (start["x"], start["y"]) end = (end["x"], end["y"]) return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) - def our_direction(self, snake_id): - head = self.all_snakes_dict[snake_id]["head"] - neck = self.all_snakes_dict[snake_id]["neck"] + @staticmethod + def snake_compass(head: dict, neck: dict) -> str: + """ + Return the direction a snake is facing in the current board + + :param head: The location of the snake's head as a dictionary e.g. {"x": 5, "y": 10} + :param neck: The location of the snake's neck as a dictionary e.g. {"x": 6, "y": 10} + + :return: Either "left", "right", "up", or "down" e.g. "right" for the above inputs + """ if neck["x"] < head["x"]: direction = "right" elif neck["x"] > head["x"]: @@ -168,113 +225,186 @@ def our_direction(self, snake_id): elif neck["y"] > head["y"]: direction = "down" else: - direction = None + direction = "none" # At the beginning of the game when snakes are coiled return direction - def peripheral_vision(self, snake_id, direction): - """Return [x1, x2] and [y1, y2] of a portion of the board that functions as 'peripheral vision' for the snake""" + def peripheral_vision(self, snake_id: str, direction: str) -> tuple[tuple, tuple, dict]: + """ + Calculate our snake's peripheral vision aka the portion of the board that is closest to our snake in a certain + direction. E.g. the space bounded by [x1, x2] and [y1, y2] in the following example board. Notice that the space + extends 3 squares above, below, and to the left of the snake, assuming we specified direction="left". Also + notice that the head of the snake doesn't actually enter the 3x7 peripheral field - the hypothetical new head is + returned as an output for convenience. + + | | | + | | | + | | | + | | | £| + | | $| + | x| x| + | | | + + :param snake_id: The ID of the desired snake we want to find the peripheral field for + :param direction: The direction you'd like to point the snake towards (either "left", "right", "up", or "down", + but use "auto" if you want to just use the direction the snake is facing in the current board) + + :return: + [x1, x2] of a portion of the board that functions as the snake's peripheral vision + [y1, y2] of the same portion + The position of the snake's head if it hypothetically moved into its peripheral field (used to perform + flood-fill on the peripheral field) + """ # Our peripheral field of vision when scanning for moves head = self.all_snakes_dict[snake_id]["head"].copy() neck = self.all_snakes_dict[snake_id]["neck"].copy() - dim = self.peripheral_dim + dim = self.peripheral_size - if direction == "auto": # Got to figure out the direction ourselves - direction = self.our_direction(snake_id) + # Got to figure out the direction ourselves + if direction == "auto": + direction = self.snake_compass(head, neck) head = neck.copy() # Roll back our head location - if direction == "right": # Neck is left of head, don't move left + # Construct the bounds of the peripheral field depending on the requested direction + if direction == "right": peripheral_box_x = head["x"] + 1, min(head["x"] + dim + 1, self.board_width) peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) head["x"], head["y"] = 0, head["y"] - peripheral_box_y[0] - elif direction == "left": # Neck is right of head, don't move right + elif direction == "left": peripheral_box_x = max(head["x"] - dim, 0), head["x"] peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) head["x"], head["y"] = max(head["x"] - peripheral_box_x[0] - 1, 0), head["y"] - peripheral_box_y[0] - elif direction == "up": # Neck is below head, don't move down + elif direction == "up": peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) peripheral_box_y = head["y"] + 1, min(head["y"] + dim + 1, self.board_height) head["x"], head["y"] = head["x"] - peripheral_box_x[0], 0 - elif direction == "down": # Neck is above head, don't move up + elif direction == "down": peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) peripheral_box_y = max(head["y"] - dim, 0), head["y"] head["x"], head["y"] = head["x"] - peripheral_box_x[0], max(head["y"] - peripheral_box_y[0] - 1, 0) - else: # Centred around our snake's head + else: # Centre it around our snake's head peripheral_box_x = max(head["x"] - dim, 0), min(head["x"] + dim + 1, self.board_width) peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) head["x"], head["y"] = head["x"] - peripheral_box_x[0], head["y"] - peripheral_box_y[0] return peripheral_box_x, peripheral_box_y, head - def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, sort_by_peripheral=False): - """Return a list of valid moves for any hypothetical snake. If risk_averse is True, avoid any moves that may - potentially cause a collision (but only if the snake is shorter) - - Use cases: - - self.get_obvious_moves(snake_id) will return moves for any opponent snake for the current board - - self.get_obvious_moves(risk_averse=True) will return possible moves that avoid death-inducing collisions with - our snake. Set False to assume our opponents are out to get us (paranoid minimax) - - self.get_obvious_moves(sort_by_dist_to=snake["id"]) will return all possible moves, but sorted by the distance - from our snake post-move to the head of the snake whose ID is given as the parameter input - - self.get_obvious_moves(sort_by_peripheral=True) will return all possible moves, but sorted by the amount of - space that each move is estimated to give us in our "peripheral vision" + def is_move_safe( + self, + move: dict, + snake_id: str, + turn: Optional[str] = "done" + ) -> tuple[bool, bool]: """ - risky_moves = [] # Initialise a set of possible risky moves - possible_moves = {"up", "down", "left", "right"} # Initialise a set of possible moves - - head = self.all_snakes_dict[snake_id]["head"] - neck = self.all_snakes_dict[snake_id]["neck"] - length = self.all_snakes_dict[snake_id]["length"] - - clock_in = time.time_ns() - if neck["x"] < head["x"]: # Neck is left of head, don't move left - possible_moves.discard("left") - elif neck["x"] > head["x"]: # Neck is right of head, don't move right - possible_moves.discard("right") - elif neck["y"] < head["y"]: # Neck is below head, don't move down - possible_moves.discard("down") - elif neck["y"] > head["y"]: # Neck is above head, don't move up - possible_moves.discard("up") + Determine if a location on the board is safe (e.g. if it's out-of-bounds or hits a different snake) or risky + (e.g. if there's a chance of a head-to-head collision). Can be used in the middle of running the minimax + algorithm (but specify the "turn_over" parameter). + + :param move: The location of the snake's hypothetical head as a dictionary e.g. {"x": 5, "y": 10} + :param snake_id: The ID of the desired snake we're evaluating a move for + :param turn: Either "ours", "opponents", or "done". Addresses nuances with running this function during the + minimax algorithm or outside of it. If "ours", this means we're at a depth where our snake has to make a + move. If "opponents", then we're at a depth where we've made a move but the opponent snakes haven't. If + "done", then both our snake and the opponent snakes have made moves (and one complete turn + has been completed). + :return: + True if the square is safe, False otherwise + True if the square is risky, False otherwise + """ # Prevent snake from moving out of bounds - if head["x"] == 0: - possible_moves.discard("left") - if head["x"] + 1 == self.board_height: - possible_moves.discard("right") - if head["y"] == 0: - possible_moves.discard("down") - if head["y"] + 1 == self.board_width: - possible_moves.discard("up") + if move["x"] < 0 or move["x"] >= self.board_width: + return False, True + if move["y"] < 0 or move["y"] >= self.board_height: + return False, True # Prevent snake from colliding with other snakes - for opp_snake_id, opp_snake in self.all_snakes_dict.items(): - for move in possible_moves.copy(): # Remaining moves - possible_hit = self.look_ahead(head, move) - # If the potential move hits a snake's body, it's invalid (exclude the tail since it moves forward) - if snake_id == self.my_id and possible_hit in opp_snake["body"][:-1]: - possible_moves.discard(move) - elif snake_id != self.my_id and possible_hit != self.my_head and ( - (possible_hit in opp_snake["body"][:-1] and opp_snake_id != self.my_id) - or (possible_hit in opp_snake["body"] and opp_snake_id == self.my_id)): - possible_moves.discard(move) - # If the snake is risk-averse, avoid any chance of head-on collisions only if our snake is shorter - elif snake_id != opp_snake_id and length <= opp_snake["length"] \ - and self.manhattan_distance(possible_hit, opp_snake["head"]) <= 1: - # Allow opponents to be suicidal (so our snakes are the same size) - if not (snake_id != self.my_id and opp_snake_id == self.my_id and length == opp_snake["length"]): - risky_moves.append(move) - if risk_averse: - possible_moves.discard(move) + length = self.all_snakes_dict[snake_id]["length"] + risky_flag = False + for opp_id, opp_snake in self.all_snakes_dict.items(): + # Different rules apply during the middle of running minimax, depending on whose turn it is since our snake + # makes moves separately from opponent snakes + if turn == "ours": + # We can run into the tail of any snake since it will have to move + if move in opp_snake["body"][:-1] and snake_id != opp_id: + return False, True + elif move in opp_snake["body"][1:] and snake_id == opp_id: + return False, True + elif (snake_id != opp_id # Skip the same snake we're evaluating + and length <= opp_snake["length"] # Only if the other snake is the same length or longer + and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound + risky_flag = True + + elif turn == "opponents": + if opp_id == self.my_id: + if move in opp_snake["body"][1:]: # Our snake's tail is off-limits since we already moved + return False, True + # Suicidal head-to-head collisions with our snake are fine (if the lengths are the same) + elif move == opp_snake["head"] and length < opp_snake["length"]: + return False, True + else: + if move in opp_snake["body"][:-1]: # Tail is fine against other opponents + return False, True + elif (snake_id != opp_id # Skip the same snake we're evaluating + and length <= opp_snake["length"] # Only if the other snake is the same length or longer + and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound + risky_flag = True + + elif turn == "done": + # Move is invalid if it collides with the body of a snake + if move in opp_snake["body"][1:]: + return False, True + # Move is invalid if it collides with the head of a snake that is the same length or longer + elif snake_id != opp_id and move == opp_snake["head"] and length <= opp_snake["length"]: + return False, True + + return True, risky_flag + + def get_obvious_moves( + self, + snake_id: str, + risk_averse: Optional[bool] = True, + sort_by_dist_to: Optional[str] = None, + sort_by_peripheral: Optional[bool] = False + ) -> list: + """ + Return a list of valid moves for any hypothetical snake. + + :param snake_id: The ID of the desired snake we want to find moves for (can also be any opponent snake). + :param risk_averse: Return possible moves that avoid death-inducing collisions (essentially we're assuming our + opponents are out to get us, but only if we're shorter). Set False to include any risky moves towards other + snakes that might kill us. + :param sort_by_dist_to: Input any snake ID here. This will return all possible moves, but sort the moves by the + distance from our snake (after making the move) to the head of the snake whose ID was inputted. Very useful + for discerning which moves are more threatening/bring us closer to a different snake. + :param sort_by_peripheral: If True, return all possible moves, but sort the moves by the amount of space that + each move will give us in our "peripheral vision". Very useful for discerning which moves allow us more + immediate space. + + :return: A list of possible moves for the given snake + """ + # Loop through possible moves and remove from consideration if it's invalid + possible_moves = ["up", "down", "left", "right"] + risky_moves = [] + head = self.all_snakes_dict[snake_id]["head"] + for move in possible_moves.copy(): + is_safe, is_risky = self.is_move_safe( + self.look_ahead(head, move), + snake_id, + turn="ours" if snake_id == self.my_id else "opponents" + ) + if not is_safe: + possible_moves.remove(move) + if is_risky: + risky_moves.append(move) if sort_by_dist_to is not None: head2 = self.all_snakes_dict[sort_by_dist_to]["head"] - possible_moves = sorted(possible_moves, # This converts a set to a list anyway + possible_moves = sorted(possible_moves, key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) elif sort_by_peripheral: - possible_moves = sorted(possible_moves, # This converts a set to a list anyway + possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), reverse=True) - else: - possible_moves = list(possible_moves) # De-prioritise any risky moves and send them to the back if len(risky_moves) > 0: @@ -282,80 +412,36 @@ def get_obvious_moves(self, snake_id, risk_averse=True, sort_by_dist_to=None, so if risky in possible_moves: possible_moves.append(possible_moves.pop(possible_moves.index(risky))) - logging.info(f"Identified obvious moves {list(possible_moves)} in " - f"{round((time.time_ns() - clock_in) / 1000000, 3)} ms") + logging.info(f"Found obvious moves {possible_moves}") return possible_moves - def optimal_move(self): - """Let's run the minimax algorithm with alpha-beta pruning!""" - # Compute the best score of each move using the minimax algorithm with alpha-beta pruning - if self.turn < 3: # Our first 3 moves are super self-explanatory tbh - search_depth = 1 - elif len(self.opponents) > 6: - search_depth = 2 # TODO should be risk-averse - elif len(self.opponents) >= 4: - search_depth = 4 - else: - search_depth = self.minimax_search_depth - - tree_tracker[search_depth].append(0) - _, best_move, _ = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) - - # Output a visualisation of the minimax decision tree for debugging - if self.debugging: - import networkx as nx - G = nx.Graph() - node_labels = {} - for node in tree_nodes: - G.add_node(node[0]) - node_labels[node[0]] = node[1] - G.add_node(0) - node_labels[0] = self.display_board(return_string=True) - G.add_edges_from(tree_edges) - pos = hierarchy_pos(G, 0) - edge_colours = [G[u][v]["colour"] for u, v in G.edges()] - edge_widths = [G[u][v]["width"] for u, v in G.edges()] + def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> tuple[bool, bool]: + """ + Determine if the game ended for certain snakes or not. Mostly use to know whether our snake died, but can + optionally be used to determine any number of opponent snakes' statuses. - fig = plt.figure(figsize=(50, 25)) - nx.draw(G, pos=pos, node_color=["white"] * G.number_of_nodes(), edge_color=edge_colours, width=edge_widths, - labels=node_labels, with_labels=True, node_size=40000, font_size=20) - plt.savefig("minimax_tree.png", bbox_inches="tight", pad_inches=0) + :param for_snake_id: The ID of the desired snake we want to know died or not + :param depth: During minimax, things get complicated when we call this function right after making a move for + our snake but before the opponent snakes have made moves. We only want to return True when a complete turn + is done (e.g. our snake made a move and our opponents did as well). Thus, we need the current depth that + minimax is on to determine this. - return best_move + :return: + True if the overall game has a winner or if our snake is dead, False otherwise + True if the snake associated with the input snake ID is alive, False otherwise + """ + # Skip if we're at the beginning of the game when all snakes are still coiled up + if self.turn == 0: + return False, True - def is_game_over(self, for_snake_id=None, depth=None): - """Return True if the game ended for our snake, False otherwise. Use for_snake_id to return a boolean for a - specific snake's status (can be one or multiple).""" snake_monitor = {} # A dictionary for each snake showing whether they're alive for snake_id, snake in self.all_snakes_dict.items(): - snake_monitor[snake_id] = True - - head = snake["head"] - # Prevent snake from moving out of bounds - if head["x"] < 0 or head["x"] >= self.board_height: - snake_monitor[snake_id] = False - if head["y"] < 0 or head["y"] >= self.board_width: - snake_monitor[snake_id] = False - - # Skip this if we're at the beginning of the game when all snakes are still coiled up - if not (snake["length"] == 3 and snake["body"][-2] == snake["body"][-1]): - # Prevent snake from colliding with any other snakes - for opp_snake_id, opp_snake in self.all_snakes_dict.items(): - # Depending on the depth, exclude the tail if the turn technically isn't over (all players need to - # have made a move) - check_body = opp_snake["body"][1:-1] if depth % 2 == 1 else opp_snake["body"][1:] - - # If the potential move hits a snake's body, it's invalid - if head in check_body: - snake_monitor[snake_id] = False - # If the potential move hits a snake's head, it's invalid only if our snake is shorter - if snake_id != opp_snake_id: - if head == opp_snake["head"] and snake["length"] <= opp_snake["length"]: - snake_monitor[snake_id] = False + # Check if each snake's head is in a safe square, depending on if we're at a depth where only we made a move + is_safe, _ = self.is_move_safe(snake["head"], snake_id, turn="done" if depth % 2 == 0 else "ours") + snake_monitor[snake_id] = is_safe # Game is over if there's only one snake remaining or if our snake died game_over = True if (sum(snake_monitor.values()) == 1 or not snake_monitor[self.my_id]) else False - # See if a specific snake is alive or not if isinstance(for_snake_id, (list, tuple)): snake_still_alive = [snake_monitor[snake_id] for snake_id in for_snake_id] @@ -369,7 +455,7 @@ def edge_kill_detection(self): return False possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) - direction = self.our_direction(self.my_id) + direction = self.snake_compass(self.my_head, self.my_neck) if self.my_head["x"] == 0: if "right" not in possible_moves: suspect = [opp_id for opp_id, opp in self.opponents.items() @@ -394,12 +480,12 @@ def edge_kill_detection(self): gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): return True - if self.my_head["y"] == 0: - if "up" not in possible_moves: - return True - if self.my_head["y"] == self.board_height - 1: - if "down" not in possible_moves: - return True + # if self.my_head["y"] == 0: + # if "up" not in possible_moves: + # return True + # if self.my_head["y"] == self.board_height - 1: + # if "down" not in possible_moves: + # return True return False def heuristic(self, depth_number): @@ -436,6 +522,7 @@ def heuristic(self, depth_number): possible_edged = self.edge_kill_detection() if possible_edged: space_penalty = -1e5 + self.edge_kill_detection() # Estimate the space we have in our peripheral vision available_peripheral = self.flood_fill(self.my_id, confined_area="auto") @@ -445,7 +532,7 @@ def heuristic(self, depth_number): # if len(self.opponents) <= 3: # # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ # # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): - # self.peripheral_dim = 4 + # self.peripheral_size = 4 # available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") # else: # available_enemy_space = 0 @@ -543,11 +630,11 @@ def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_m else: board = np.full((self.board_width, self.board_height), " ") for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): - board[square["x"], square["y"]] = "£" if num == 0 else "O" + board[square["x"], square["y"]] = "£" if num == 0 else "o" for other_id, other_snake in self.all_snakes_dict.items(): if other_id != snake_id: for num, other_square in enumerate(other_snake["body"]): - board[other_square["x"], other_square["y"]] = "$" if num == 0 else "X" + board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" if confined_area is not None: xs, ys, head = self.peripheral_vision(snake_id, confined_area) @@ -556,9 +643,9 @@ def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_m # self.peripheral_vision(snake_id, confined_area) def fill(x, y, board, initial_square): - if board[x][y] in ["X", "$"]: # Snakes or hazards + if board[x][y] in ["x", "$"]: # Snakes or hazards return - if board[x][y] in ["O"]: # Us + if board[x][y] in ["o"]: # Us return if board[x][y] == "£" and not initial_square: # Already filled return @@ -713,6 +800,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"Heuristic = {heuristic} at terminal node") return heuristic, None, heuristic_data + global tree_edges # Our snake's turn if maximising_snake: logging.info("=" * 50) @@ -763,7 +851,6 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"PRUNED!!! Alpha = {alpha} >= Beta = {beta}") break - global tree_edges tree_edges[best_edge][2]["colour"] = "r" tree_edges[best_edge][2]["width"] = 4 logging.info(f"FINISHED MINIMAX LAYER on our snake in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") @@ -822,7 +909,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): else: cutoff = 3 - if len(opps_moves) > 0: + if len(opps_moves) > 0 and len(all_opp_combos) > cutoff: covered_ids = [list(opps_moves.keys())[0]] all_opp_combos2 = [] while len(all_opp_combos2) < cutoff: @@ -891,6 +978,43 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"FINISHED MINIMAX LAYER on opponents in {(time.time_ns() - clock_in) // 1000000} ms") return best_val, best_move, best_node_data + def optimal_move(self): + """Let's run the minimax algorithm with alpha-beta pruning!""" + # Compute the best score of each move using the minimax algorithm with alpha-beta pruning + if self.turn < 3: # Our first 3 moves are super self-explanatory tbh + search_depth = 1 + elif len(self.opponents) > 6: + search_depth = 2 # TODO should be risk-averse + elif len(self.opponents) >= 4: + search_depth = 4 + else: + search_depth = self.minimax_search_depth + + tree_tracker[search_depth].append(0) + _, best_move, _ = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) + + # Output a visualisation of the minimax decision tree for debugging + if self.debugging: + import networkx as nx + G = nx.Graph() + node_labels = {} + for node in tree_nodes: + G.add_node(node[0]) + node_labels[node[0]] = node[1] + G.add_node(0) + node_labels[0] = self.display_board(return_string=True) + G.add_edges_from(tree_edges) + pos = hierarchy_pos(G, 0) + edge_colours = [G[u][v]["colour"] for u, v in G.edges()] + edge_widths = [G[u][v]["width"] for u, v in G.edges()] + + fig = plt.figure(figsize=(50, 25)) + nx.draw(G, pos=pos, node_color=["white"] * G.number_of_nodes(), edge_color=edge_colours, width=edge_widths, + labels=node_labels, with_labels=True, node_size=40000, font_size=20) + plt.savefig("minimax_tree.png", bbox_inches="tight", pad_inches=0) + + return best_move + # def a_star_search(self, food_loc): # """ # Implement the A* search algorithm to find the shortest path to food From e331ae6d98b3a01acc3a08128ac888ee86c5c397 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 24 Oct 2023 22:20:26 +0100 Subject: [PATCH 39/64] New --- snake_engine.py | 59 +++++++++++++++++++++++++++++-------------------- 1 file changed, 35 insertions(+), 24 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 3efe1a2a..e76d63a5 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -451,23 +451,26 @@ def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> return game_over, snake_still_alive def edge_kill_detection(self): + """ + Determine if our snake is in a position where it can get edge-killed + """ if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: return False possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) direction = self.snake_compass(self.my_head, self.my_neck) - if self.my_head["x"] == 0: - if "right" not in possible_moves: - suspect = [opp_id for opp_id, opp in self.opponents.items() - if opp["head"]["x"] == self.my_head["x"] + 1 and ( - (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) - or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) - )] - if len(suspect) > 0: - diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) - gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to - if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): - return True + if self.my_head["x"] == 0 and "right" not in possible_moves: + # Find snakes that can possibly edge-kill us + suspect = [opp_id for opp_id, opp in self.opponents.items() + if opp["head"]["x"] == self.my_head["x"] + 1 and ( + (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) + or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) + )] + if len(suspect) > 0: + diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) + gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to + if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): + return True if self.my_head["x"] == self.board_width - 1: if "left" not in possible_moves: suspect = [opp_id for opp_id, opp in self.opponents.items() @@ -527,15 +530,20 @@ def heuristic(self, depth_number): # Estimate the space we have in our peripheral vision available_peripheral = self.flood_fill(self.my_id, confined_area="auto") - # # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in - # # our vicinity) - # if len(self.opponents) <= 3: - # # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ - # # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): - # self.peripheral_size = 4 - # available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") - # else: - # available_enemy_space = 0 + # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in + # our vicinity) + if len(self.opponents) <= 3: + # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ + # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + self.peripheral_size = 4 + available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") + if available_enemy_space < 4: + kill_bonus = 1000 + else: + kill_bonus = 0 + else: + available_enemy_space = 0 + kill_bonus = 0 # Get closer to enemy snakes if we're longer by 3 if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): @@ -577,7 +585,8 @@ def heuristic(self, depth_number): logging.info(f"Distance to nearest enemy: {dist_to_enemy}") logging.info(f"Distance to nearest food: {dist_food}") logging.info(f"Layers deep in search tree: {layers_deep}") - # logging.info(f"Available enemy space: {available_enemy_space}") + logging.info(f"Available enemy space: {available_enemy_space}") + logging.info(f"Kill bonus: {kill_bonus}") logging.info(f"In centre: {in_centre}") logging.info(f"Length: {self.my_length}") @@ -589,8 +598,9 @@ def heuristic(self, depth_number): (layers_deep * depth_weight) + \ (self.my_length * length_weight) + \ in_centre * centre_control_weight + \ - aggression_weight / (dist_to_enemy + 1) -# (enemy_restriction_weight / (available_enemy_space + 1)) + \ + aggression_weight / (dist_to_enemy + 1) + \ + (enemy_restriction_weight / (available_enemy_space + 1)) + kill_bonus + return h, {"Heur": round(h, 2), "Space": available_space, @@ -598,6 +608,7 @@ def heuristic(self, depth_number): "Periph": available_peripheral, "Food Dist": dist_food, "Enemy Dist": dist_to_enemy, + "Enemy Kill": available_enemy_space + kill_bonus, "Threats": num_threats, "Length": self.my_length} From 7c1f42bdcb2407ecedebe4c0a92f3a848be22a53 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 24 Oct 2023 22:50:55 +0100 Subject: [PATCH 40/64] New 2 --- snake_engine.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index e76d63a5..87aa1595 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -321,6 +321,7 @@ def is_move_safe( length = self.all_snakes_dict[snake_id]["length"] risky_flag = False for opp_id, opp_snake in self.all_snakes_dict.items(): + # Different rules apply during the middle of running minimax, depending on whose turn it is since our snake # makes moves separately from opponent snakes if turn == "ours": @@ -405,7 +406,6 @@ def get_obvious_moves( possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), reverse=True) - # De-prioritise any risky moves and send them to the back if len(risky_moves) > 0: for risky in risky_moves: @@ -508,7 +508,10 @@ def heuristic(self, depth_number): # Determine available space via flood fill available_space = self.flood_fill(self.my_id, risk_averse=True) - if available_space < 4: + available_space_ra = self.flood_fill(self.my_id, risk_averse=False) + if available_space_ra < 4: + space_penalty = -500 + elif available_space < 4: space_penalty = -200 else: space_penalty = 0 @@ -636,7 +639,7 @@ def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_m for n in avoid_sq: if not (n[0] == head["x"] and n[1] == head["y"]) and \ (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): - board[n[0]][n[1]] = "X" + board[n[0]][n[1]] = "x" # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) else: board = np.full((self.board_width, self.board_height), " ") From eb4436e37332af49a2471aa900f2a5bc169a5d75 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 25 Oct 2023 10:57:35 +0100 Subject: [PATCH 41/64] A* --- snake_engine.py | 458 ++++++++++++++++++++++++------------------------ 1 file changed, 227 insertions(+), 231 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 87aa1595..99fd708a 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -2,6 +2,7 @@ import copy import itertools import logging +import networkx as nx import numpy as np import sys import time @@ -32,7 +33,8 @@ def __init__(self, game_state: dict, debugging: Optional[bool] = False): self.board_height = game_state["board"]["height"] self.food = game_state["board"]["food"] self.hazards = game_state["board"]["hazards"] - self.board = np.full((self.board_width, self.board_height), " ") + self.board = None # Generated later with update_board() + self.graph = None # Generated later with update_board() # Our snake's data self.my_id = game_state["you"]["id"] @@ -124,12 +126,16 @@ def update_board(self): represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner. """ self.board = np.full((self.board_width, self.board_height), " ") + self.graph = nx.grid_2d_graph(self.board_width, self.board_height) for num, my_square in enumerate(self.my_body): self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" + if num > 0: + self.graph.remove_nodes_from([(my_square["x"], my_square["y"])]) for opponent in self.opponents.values(): snake_body = opponent["body"] for num, snake_sq in enumerate(snake_body): self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "x" + self.graph.remove_nodes_from([(snake_sq["x"], snake_sq["y"])]) def display_board(self, board: Optional[np.array] = None, return_string: Optional[bool] = False): """ @@ -156,7 +162,10 @@ def display_board(self, board: Optional[np.array] = None, return_string: Optiona display_row = "" for i in range(0, len(render_board)): display_row += f"{render_board[i][-j]}| " - logging.info(display_row) + if self.debugging: + logging.info(display_row) + else: + print(display_row) # Return the board as a string instead of printing it out if return_string: @@ -195,7 +204,7 @@ def look_ahead(head: dict, move: str) -> dict: @staticmethod def manhattan_distance(start: dict, end: dict) -> int: """ - Return the Manhattan distance for two positions + Return the Manhattan distance between two positions :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} :param end: A different location on the board @@ -206,6 +215,26 @@ def manhattan_distance(start: dict, end: dict) -> int: end = (end["x"], end["y"]) return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) + def dijkstra_shortest_path(self, start, end): + """ + Return the shortest path between two positions using Dijkstra's algorithm implemented in networkx + + :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} + :param end: A different location on the board + + :return: The shortest distance between the start and end inputs. 1e6 if no path could be found + """ + start = (start["x"], start["y"]) + end = (end["x"], end["y"]) + self.graph.add_node(end) + try: + path = nx.shortest_path(self.graph, start, end) + self.graph.remove_node(end) + return len(path) + except nx.exception.NetworkXNoPath: + self.graph.remove_node(end) + return 1e6 + @staticmethod def snake_compass(head: dict, neck: dict) -> str: """ @@ -297,7 +326,7 @@ def is_move_safe( """ Determine if a location on the board is safe (e.g. if it's out-of-bounds or hits a different snake) or risky (e.g. if there's a chance of a head-to-head collision). Can be used in the middle of running the minimax - algorithm (but specify the "turn_over" parameter). + algorithm (but make sure to specify the "turn_over" parameter). :param move: The location of the snake's hypothetical head as a dictionary e.g. {"x": 5, "y": 10} :param snake_id: The ID of the desired snake we're evaluating a move for @@ -325,11 +354,13 @@ def is_move_safe( # Different rules apply during the middle of running minimax, depending on whose turn it is since our snake # makes moves separately from opponent snakes if turn == "ours": - # We can run into the tail of any snake since it will have to move + # We can run into the tail of any snake since it will have to move forward if move in opp_snake["body"][:-1] and snake_id != opp_id: return False, True - elif move in opp_snake["body"][1:] and snake_id == opp_id: + # We cannot hit our own head + elif move in opp_snake["body"][1:-1] and snake_id == opp_id: return False, True + # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating and length <= opp_snake["length"] # Only if the other snake is the same length or longer and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound @@ -337,14 +368,18 @@ def is_move_safe( elif turn == "opponents": if opp_id == self.my_id: - if move in opp_snake["body"][1:]: # Our snake's tail is off-limits since we already moved + # Our snake's tail is off-limits since we will already have moved + if move in opp_snake["body"][1:]: return False, True - # Suicidal head-to-head collisions with our snake are fine (if the lengths are the same) + # Avoid losing head-to-head collisions with our snake, but suicidal collisions are fine if our snake + # is the same length and also dies elif move == opp_snake["head"] and length < opp_snake["length"]: return False, True else: - if move in opp_snake["body"][:-1]: # Tail is fine against other opponents + # Tail is fine against other opponents + if move in opp_snake["body"][:-1]: return False, True + # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating and length <= opp_snake["length"] # Only if the other snake is the same length or longer and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound @@ -358,6 +393,10 @@ def is_move_safe( elif snake_id != opp_id and move == opp_snake["head"] and length <= opp_snake["length"]: return False, True + elif turn == "flood_fill": + if move in opp_snake["body"]: + return False, True + return True, risky_flag def get_obvious_moves( @@ -393,7 +432,7 @@ def get_obvious_moves( snake_id, turn="ours" if snake_id == self.my_id else "opponents" ) - if not is_safe: + if not is_safe or (risk_averse and is_risky): possible_moves.remove(move) if is_risky: risky_moves.append(move) @@ -401,8 +440,8 @@ def get_obvious_moves( if sort_by_dist_to is not None: head2 = self.all_snakes_dict[sort_by_dist_to]["head"] possible_moves = sorted(possible_moves, - key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) - elif sort_by_peripheral: + key=lambda move2: self.dijkstra_shortest_path(head2, self.look_ahead(head, move2))) + if sort_by_peripheral: possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), reverse=True) @@ -450,6 +489,171 @@ def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> return game_over, snake_still_alive + def simulate_move(self, move: str, snake_id: str, evaluate_deaths: Optional[bool] = False) -> Battlesnake: + """ + Create a new Battlesnake instance that simulates a move for a given snake + + :param move: The direction you'd like the snake to move towards (either "left", "right", "up", or "down") + :param snake_id: The ID of the desired snake we want to simulate a move for + :param evaluate_deaths: If True, remove any snakes that died as a result of the simulated move (exclusively via + head-to-head collisions) + + :return: A Battlesnake instance incorporating the simulated move in a new game state + """ + # clock_in = time.time_ns() + new_game = self.__copy__() + + # Simulate the new head position + old_head = self.all_snakes_dict[snake_id]["head"] + new_head = self.look_ahead(old_head, move) + + # Insert the simulated snake position into the new instance + new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] + new_game.all_snakes_dict[snake_id]["head"] = new_head + new_game.all_snakes_dict[snake_id]["health"] -= 1 + # Repeat for our snake's specific attributes + if snake_id == self.my_id: + new_game.my_body = [new_head] + new_game.my_body[:-1] + new_game.my_head = new_head + new_game.my_health -= 1 + + # Track if food was consumed - this elongates the snake from the tail and restores health + if new_head in self.food: + new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head + + # Check if any snakes died from this simulated move and remove them from the game + if evaluate_deaths: + # First update snake lengths from any food eaten + for update_id, snake in new_game.all_snakes_dict.items(): + if len(snake["food_eaten"]) > 0: + new_game.all_snakes_dict[update_id]["length"] += 1 + new_game.all_snakes_dict[update_id]["health"] = 100 + new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] + new_game.food = [food for food in self.food # Remove the food from the board + if not (food["x"] == snake["food_eaten"]["x"] + and food["y"] == snake["food_eaten"]["y"])] + if update_id == self.my_id: + new_game.my_length += 1 + new_game.my_health = 100 + new_game.my_body += [new_game.my_body[-1]] + # Reset the food tracker + new_game.all_snakes_dict[update_id]["food_eaten"] = [] + + # Did any snakes die from head-to-head collisions? + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] + count_heads = Counter(all_heads) + butt_heads = [k for k, v in count_heads.items() if v > 1] # Any square where > 1 heads collided + for butt_head in butt_heads: + overlapping_snakes = np.array([ + (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() + if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) + ]) + lengths = overlapping_snakes[:, 1].astype(int) + # Special cases where the snake committed suicide and also killed our snake => don't remove + if not (self.my_id in overlapping_snakes[:, 0] + and np.count_nonzero(lengths == new_game.my_length) > 1): + indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() + if len(indices_largest_snakes) > 1: # No winner if the snakes are the same length + winner_id = None + else: + winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] + # Remove any dead snakes + for rm_id in overlapping_snakes[:, 0]: + if rm_id != winner_id: + new_game.all_snakes_dict.pop(rm_id) + + new_game.update_board() + # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + return new_game + + def flood_fill( + self, + snake_id: str, + confined_area: Optional[str] = None, + risk_averse: Optional[bool] = False, + fast_forward: Optional[int] = 0 + ) -> int: + """ + Recursive function to get the total available space for a given snake. Basically, count how many £ symbols + we can fill while avoiding any $, o, and x symbols (obstacles). + + :param snake_id: The ID of the desired snake we want to do flood fill for + :param confined_area: Tells the function to do flood fill for only on one side of the snake (either "left", + "right", "up", or "down") to represent its peripheral vision + :param risk_averse: If True, flood fill will avoid any squares that directly border an opponent's head + :param fast_forward: Hypothetical scenarios where we want to see how much space we still have after moving + X turns ahead. E.g. if we set it to 5, then we remove 5 squares from all snake's tails before doing flood + fill - this is only useful when we suspect we'll be trapped by an opponent snake. + + :return: The total area of the flood fill selection + """ + head = self.all_snakes_dict[snake_id]["head"] + + if snake_id == self.my_id: # Assume we're doing flood fill for our snake + board = copy.deepcopy(self.board) + # See how flood fill changes when all snakes fast-forward X turns + if fast_forward > 0: + for snake in self.all_snakes_dict.values(): + tail_removed = snake["body"][-fast_forward:] + for remove in tail_removed: + board[remove["x"]][remove["y"]] = " " + # Try to avoid any squares that our enemy can go to + if risk_averse: + threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] + for threat in threats: + x, y = threat["x"], threat["y"] + avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in avoid_sq: + if not (n[0] == head["x"] and n[1] == head["y"]) and \ + (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): + board[n[0]][n[1]] = "x" + else: # Otherwise, generate a new board and pretend the opponent snake is our snake + board = np.full((self.board_width, self.board_height), " ") + for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): + board[square["x"], square["y"]] = "£" if num == 0 else "o" + for other_id, other_snake in self.all_snakes_dict.items(): + if other_id != snake_id: + for num, other_square in enumerate(other_snake["body"]): + board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" + + # Narrow down a portion of the board that represents the snake's peripheral vision + if confined_area is not None: + xs, ys, head = self.peripheral_vision(snake_id, confined_area) + board = board[xs[0]:xs[1], ys[0]:ys[1]] + + def fill(x, y, board, initial_square): + if board[x][y] in ["x", "$"]: # Snakes or hazards + return + if board[x][y] in ["o"]: # Our snake + return + if board[x][y] == "£" and not initial_square: # Already filled + return + board[x][y] = "£" + neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in neighbours: + if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): + fill(n[0], n[1], board, initial_square=False) + + fill(head["x"], head["y"], board, initial_square=True) + filled = sum((row == "£").sum() for row in board) + return filled - 1 if filled > 0 else filled # Exclude the head from the count, but cannot ever be negative + + def dist_to_nearest_food(self) -> int: + """ + Return the shortest distance to food for our snake, but only if we're closer to it than an opponent snake + """ + best_dist = np.inf + for food in self.food: + dist = self.dijkstra_shortest_path(food, self.my_head) + # If an enemy snake is longer than ours, and we're both 2 squares away from food, then they're technically + # closer to it since they'd win the head-to-head battle. + dist_enemy = min([self.dijkstra_shortest_path(food, snake["head"]) if snake["length"] < self.my_length + else self.dijkstra_shortest_path(food, snake["head"]) - 1 + for snake in self.opponents.values()]) + if dist < best_dist and dist_enemy >= dist: + best_dist = dist + return best_dist + def edge_kill_detection(self): """ Determine if our snake is in a position where it can get edge-killed @@ -519,7 +723,7 @@ def heuristic(self, depth_number): # ARE WE TRAPPED??? trap_space = None if available_space <= 10: - trap_space = available_space - self.flood_fill(self.my_id, estimate_moves=available_space) + trap_space = available_space - self.flood_fill(self.my_id, fast_forward=available_space) # Shoot we're trapped if trap_space == 0: space_penalty = -1e7 # We'd prefer getting killed than getting trapped, so penalise this more @@ -539,7 +743,8 @@ def heuristic(self, depth_number): # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): self.peripheral_size = 4 - available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") + closest_enemy = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"]))[0] + available_enemy_space = self.flood_fill(closest_enemy, confined_area="General") if available_enemy_space < 4: kill_bonus = 1000 else: @@ -550,12 +755,13 @@ def heuristic(self, depth_number): # Get closer to enemy snakes if we're longer by 3 if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): - dist_to_enemy = self.dist_from_enemies()[0] + dist_from_enemies = sorted([self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values()]) + dist_to_enemy = dist_from_enemies[0] else: dist_to_enemy = 0 # If we're getting too close to enemy snakes that are longer, RETREAT - threats = [self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] + threats = [self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] num_threats = (np.count_nonzero(np.array(threats) <= 2) * 2 + np.count_nonzero(np.array(threats) == 3) * 1) @@ -615,151 +821,6 @@ def heuristic(self, depth_number): "Threats": num_threats, "Length": self.my_length} - def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_moves=0): - """Recursive function to get the total area of the current fill selection. Basically, count how many £ symbols - we can fill while avoiding any $, O, and X symbols (obstacles). Confined_area tells the function to do flood - fill only on one side of the snake (should be either left/right/up/down).""" - head = self.all_snakes_dict[snake_id]["head"] - - # Assume we're doing flood fill for our snake - if snake_id == self.my_id: - board = copy.deepcopy(self.board) - # Let's simulate entrapment - if estimate_moves > 0: - for snake in self.all_snakes_dict.values(): - tail_removed = snake["body"][-estimate_moves:] - for remove in tail_removed: - board[remove["x"]][remove["y"]] = " " - # Let's try to avoid any squares that our enemy can go to - if risk_averse: - threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] - for threat in threats: - x, y = threat["x"], threat["y"] - avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in avoid_sq: - if not (n[0] == head["x"] and n[1] == head["y"]) and \ - (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): - board[n[0]][n[1]] = "x" - # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) - else: - board = np.full((self.board_width, self.board_height), " ") - for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): - board[square["x"], square["y"]] = "£" if num == 0 else "o" - for other_id, other_snake in self.all_snakes_dict.items(): - if other_id != snake_id: - for num, other_square in enumerate(other_snake["body"]): - board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" - - if confined_area is not None: - xs, ys, head = self.peripheral_vision(snake_id, confined_area) - board = board[xs[0]:xs[1], ys[0]:ys[1]] - # if -1 == head["x"]: - # self.peripheral_vision(snake_id, confined_area) - - def fill(x, y, board, initial_square): - if board[x][y] in ["x", "$"]: # Snakes or hazards - return - if board[x][y] in ["o"]: # Us - return - if board[x][y] == "£" and not initial_square: # Already filled - return - board[x][y] = "£" - neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in neighbours: - if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): - fill(n[0], n[1], board, initial_square=False) - - fill(head["x"], head["y"], board, initial_square=True) - filled = sum((row == "£").sum() for row in board) - return filled - 1 if filled > 0 else filled # Exclude the head, but cannot ever be negative - - def dist_to_nearest_food(self): - """Return the Manhattan distance to the nearest food for our snake""" - best_dist = np.inf - for food in self.food: - dist = self.manhattan_distance(food, self.my_head) - dist_enemy = min([self.manhattan_distance(food, snake["head"]) for snake in self.opponents.values()]) - if dist < best_dist and ((dist_enemy >= 4 and dist <= dist_enemy * 2) or (4 > dist_enemy >= dist)): - best_dist = dist - return best_dist - - def dist_from_enemies(self): - """To gauge how in danger we are, return a list of Manhattan distances to all snakes""" - return sorted([self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values()]) - - def closest_enemy(self): - """Return the nearest enemy snake""" - return sorted(self.opponents.keys(), - key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]))[0] - - def simulate_move(self, move, snake_id, evaluate_deaths=False): - """Create a new Battlesnake instance with a simulated move for a given snake""" - # clock_in = time.time_ns() - new_game = self.__copy__() - - # Simulate the new head position - old_head = self.all_snakes_dict[snake_id]["head"] - new_head = self.look_ahead(old_head, move) - - # Insert the simulated snake position into the new instance - new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] - new_game.all_snakes_dict[snake_id]["head"] = new_head - new_game.all_snakes_dict[snake_id]["health"] -= 1 - # Repeat for our snake's specific attributes - if snake_id == self.my_id: - new_game.my_body = [new_head] + new_game.my_body[:-1] - new_game.my_head = new_head - new_game.my_health -= 1 - - # Keep track if food was consumed - this elongates the snake from the tail and restores health - if new_head in self.food: - new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head - - # Check if any snakes died from this simulated move and remove them from the game - if evaluate_deaths: - # Update snake lengths from any food eaten - for update_id, snake in new_game.all_snakes_dict.items(): - if len(snake["food_eaten"]) > 0: - new_game.all_snakes_dict[update_id]["length"] += 1 - new_game.all_snakes_dict[update_id]["health"] = 100 - new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] - new_game.food = [food for food in self.food # Remove the food from the board - if not (food["x"] == snake["food_eaten"]["x"] - and food["y"] == snake["food_eaten"]["y"])] - if update_id == self.my_id: - new_game.my_length += 1 - new_game.my_health = 100 - new_game.my_body += [new_game.my_body[-1]] - # Reset the food tracker - new_game.all_snakes_dict[update_id]["food_eaten"] = [] - - # Did any snakes die from collisions? - all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] - count_heads = Counter(all_heads) - butt_heads = [k for k, v in count_heads.items() if v > 1] - for butt_head in butt_heads: - overlapping_snakes = np.array([ - (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() - if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) - ]) - lengths = overlapping_snakes[:, 1].astype(int) - # Special cases where the snake committed suicide and also killed our snake => don't remove - if not (self.my_id in overlapping_snakes[:, 0] - and np.count_nonzero(lengths == new_game.my_length) > 1): - indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() - if len(indices_largest_snakes) > 1: - winner_id = None - else: - winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] - - for rm_id in overlapping_snakes[:, 0]: - if rm_id != winner_id: - new_game.all_snakes_dict.pop(rm_id) - - new_game.update_board() - # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return new_game - @staticmethod def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data=None, insert_index=None): global tree_node_counter @@ -785,12 +846,14 @@ def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data return len(tree_nodes) - 1 def minimax(self, depth, alpha, beta, maximising_snake): - """Implement the minimax algorithm with alpha-beta pruning + """ + Implement the minimax algorithm with alpha-beta pruning :param depth: :param alpha: :param beta: :param maximising_snake: + :return: """ if depth != self.minimax_search_depth: @@ -896,7 +959,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): opp_move = ["down"] # Save time by only searching for snakes within close range - dist_opp_to_us = self.manhattan_distance(self.my_head, opp_snake["head"]) + dist_opp_to_us = self.dijkstra_shortest_path(self.my_head, opp_snake["head"]) if dist_opp_to_us <= search_within: opps_moves[opp_id] = opp_move # Put all of their moves opps_nearby += 1 @@ -904,7 +967,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(self.opponents) <= 5: opps_moves[opp_id] = [opp_move[0]] - sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"])) + sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"])) opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_opps_by_dists.index(pair[0]))) logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " @@ -1028,70 +1091,3 @@ def optimal_move(self): plt.savefig("minimax_tree.png", bbox_inches="tight", pad_inches=0) return best_move - -# def a_star_search(self, food_loc): -# """ -# Implement the A* search algorithm to find the shortest path to food -# """ -# head_loc = self.my_head.copy() -# open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet -# closed_list = [] # Contains the nodes which are examined -# timer = 0 -# -# while len(open_list) > 0: -# if timer > 300: -# return None, None -# -# current_node = min(open_list, key=lambda node: node.f) # The node with the smallest f value -# open_list.remove(current_node) # Remove from open list -# -# if current_node.location == food_loc: # Found the food! Stop search -# traceback = [food_loc] -# pointer = current_node -# while pointer.parent is not None: -# traceback.append(pointer.parent.location) -# pointer = pointer.parent -# return traceback, current_node.g -# -# # Identify all possible nodes to move to -# neighbour_nodes = [] -# possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) -# -# for possible_move in possible_moves: -# possible_node = Node(self.look_ahead(current_node.location, possible_move), parent=current_node) -# neighbour_nodes.append(possible_node) -# -# for possible_node in neighbour_nodes: # For each possible new node -# if possible_node.location in closed_list: # Skip if the node is in the closed list -# continue -# -# # Compute f value for the neighbour node -# possible_node.g = current_node.g + 1 -# possible_node.h = self.manhattan_distance(current_node.location, food_loc) -# possible_node.f = possible_node.g + possible_node.h -# -# # If the node's location is also in the open list but has a worse g value, ignore since we can do better -# for open_node in open_list: -# if possible_node.location == open_node.location and possible_node.g > open_node.g: -# break -# -# # Otherwise, this is the best we can do so far - add to open list -# open_list.append(possible_node) -# -# closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours -# timer += 1 -# -# return None, None -# -# -class Node: - def __init__(self, location, parent=None, f=0, g=0, h=0): - self.location = location - self.neighbour_nodes = [] - self.parent = parent - self.f = f # The cost from the start node to the goal node - self.g = g # The cost from the start node to the current node - self.h = h # The cost from the current node to the goal node using a heuristic - - def __repr__(self): - return str(self.location) \ No newline at end of file From 0b3087193c1b37e9558b772e5e4f22c92a607004 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 25 Oct 2023 13:14:34 +0100 Subject: [PATCH 42/64] A* --- snake_engine.py | 109 ++++++++++++++++++++++++++++++++---------------- 1 file changed, 72 insertions(+), 37 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 99fd708a..64258a87 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -215,7 +215,7 @@ def manhattan_distance(start: dict, end: dict) -> int: end = (end["x"], end["y"]) return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) - def dijkstra_shortest_path(self, start, end): + def dijkstra_shortest_path(self, start: dict, end: dict) -> int: """ Return the shortest path between two positions using Dijkstra's algorithm implemented in networkx @@ -226,14 +226,22 @@ def dijkstra_shortest_path(self, start, end): """ start = (start["x"], start["y"]) end = (end["x"], end["y"]) - self.graph.add_node(end) + + # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later + temp_node = False + if end not in self.graph.nodes(): + self.graph.add_node(end) + temp_node = True + try: path = nx.shortest_path(self.graph, start, end) - self.graph.remove_node(end) - return len(path) + shortest = len(path) except nx.exception.NetworkXNoPath: + shortest = 1e6 + + if temp_node: self.graph.remove_node(end) - return 1e6 + return shortest @staticmethod def snake_compass(head: dict, neck: dict) -> str: @@ -658,41 +666,68 @@ def edge_kill_detection(self): """ Determine if our snake is in a position where it can get edge-killed """ + # Ignore if we're not on the edge of the board if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: return False - possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) - direction = self.snake_compass(self.my_head, self.my_neck) - if self.my_head["x"] == 0 and "right" not in possible_moves: - # Find snakes that can possibly edge-kill us - suspect = [opp_id for opp_id, opp in self.opponents.items() - if opp["head"]["x"] == self.my_head["x"] + 1 and ( - (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) - or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) - )] - if len(suspect) > 0: - diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) - gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to - if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): - return True - if self.my_head["x"] == self.board_width - 1: - if "left" not in possible_moves: - suspect = [opp_id for opp_id, opp in self.opponents.items() - if opp["head"]["x"] == self.my_head["x"] - 1 and ( - (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) - or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) - )] - if len(suspect) > 0: - diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) - gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to - if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): - return True - # if self.my_head["y"] == 0: - # if "up" not in possible_moves: - # return True - # if self.my_head["y"] == self.board_height - 1: - # if "down" not in possible_moves: - # return True + # possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) + # direction = self.snake_compass(self.my_head, self.my_neck) + # dir_dict = { + # "vertical": { + # "bounds": [0, self.board_width], + # "escape_dirs": ["left", "right"], + # "axis": "x", + # "axis_dir": "y", + # "scan_dir": +1 if direction == "up" else -1 + # }, + # "horizontal": { + # "bounds": [0, self.board_height], + # "escape_dirs": ["down", "up"], + # "axis": "y", + # "axis_dir": "x", + # "scan_dir": +1 if direction == "right" else -1 + # }, + # } + # dir_data = dir_dict["horizontal"] if direction in ["left", "right"] else dir_dict["vertical"] + # bounds = dir_data["bounds"] + # escape_dirs = dir_data["escape_dirs"] + # ax = dir_data["axis"] + # ax_dir = dir_data["axis_dir"] + # scan_dir = dir_data["scan_dir"] + # + # # If we can't escape (e.g. we're heading right, but can't move up or down) + # if len(set(escape_dirs).intersection(possible_moves)) == 0: + # trapped_sides = [False, False] + # for num, escape_dir in enumerate(escape_dirs): + # look = -1 if num == 0 else +1 + # # Scan the column/row to each side of us in ascending order + # if escape_dir in ["left", "right"]: + # esc_attempt = self.my_head[ax] + look + # if 0 <= esc_attempt < self.board_width: + # strip = self.board[esc_attempt, self.my_head[ax_dir]:] + # # Check if there's free space ahead of us, we're trapped if there's none + # if np.count_nonzero(strip != " ") == 0: + # trapped_sides[num] = True + # + # threats = [opp_id for opp_id, opp in self.opponents.items() + # if (opp["head"][ax] == esc_attempt) + # and (opp["head"][ax_dir] >= self.my_head[ax_dir] if look == 1 + # else opp["head"][ax_dir] <= self.my_head[ax_dir] + # )] + # + # # # Find snakes that can possibly edge-kill us + # # suspects = [opp_id for opp_id, opp in self.opponents.items() + # # if opp["head"][ax] == self.my_head[ax] + dir_data["adj_col"] and ( + # # opp["head"][dir_data["axis_dir"]] >= self.my_head[dir_data["axis_dir"]] if dir_data["scan_dir"] == 1 + # # else opp["head"][dir_data["axis_dir"]] <= self.my_head[dir_data["axis_dir"]]) + # # ] + # # if len(suspects) > 0: + # # diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspects]) + # # if diff_y == 0: + # # return True + # # gaps = [self.opponents[opp_id]["body"][-diff_y:] for opp_id in suspects] # Would leave space for us to move to + # # if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): + # # return True return False def heuristic(self, depth_number): From f08930f1add5f2cc19fe8e3d00515208af6c86a0 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 25 Oct 2023 13:23:26 +0100 Subject: [PATCH 43/64] Reverted to previous version yesterday --- snake_engine.py | 557 +++++++++++++++++++++++------------------------- 1 file changed, 263 insertions(+), 294 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 64258a87..87aa1595 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -2,7 +2,6 @@ import copy import itertools import logging -import networkx as nx import numpy as np import sys import time @@ -33,8 +32,7 @@ def __init__(self, game_state: dict, debugging: Optional[bool] = False): self.board_height = game_state["board"]["height"] self.food = game_state["board"]["food"] self.hazards = game_state["board"]["hazards"] - self.board = None # Generated later with update_board() - self.graph = None # Generated later with update_board() + self.board = np.full((self.board_width, self.board_height), " ") # Our snake's data self.my_id = game_state["you"]["id"] @@ -126,16 +124,12 @@ def update_board(self): represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner. """ self.board = np.full((self.board_width, self.board_height), " ") - self.graph = nx.grid_2d_graph(self.board_width, self.board_height) for num, my_square in enumerate(self.my_body): self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" - if num > 0: - self.graph.remove_nodes_from([(my_square["x"], my_square["y"])]) for opponent in self.opponents.values(): snake_body = opponent["body"] for num, snake_sq in enumerate(snake_body): self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "x" - self.graph.remove_nodes_from([(snake_sq["x"], snake_sq["y"])]) def display_board(self, board: Optional[np.array] = None, return_string: Optional[bool] = False): """ @@ -162,10 +156,7 @@ def display_board(self, board: Optional[np.array] = None, return_string: Optiona display_row = "" for i in range(0, len(render_board)): display_row += f"{render_board[i][-j]}| " - if self.debugging: - logging.info(display_row) - else: - print(display_row) + logging.info(display_row) # Return the board as a string instead of printing it out if return_string: @@ -204,7 +195,7 @@ def look_ahead(head: dict, move: str) -> dict: @staticmethod def manhattan_distance(start: dict, end: dict) -> int: """ - Return the Manhattan distance between two positions + Return the Manhattan distance for two positions :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} :param end: A different location on the board @@ -215,34 +206,6 @@ def manhattan_distance(start: dict, end: dict) -> int: end = (end["x"], end["y"]) return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) - def dijkstra_shortest_path(self, start: dict, end: dict) -> int: - """ - Return the shortest path between two positions using Dijkstra's algorithm implemented in networkx - - :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} - :param end: A different location on the board - - :return: The shortest distance between the start and end inputs. 1e6 if no path could be found - """ - start = (start["x"], start["y"]) - end = (end["x"], end["y"]) - - # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later - temp_node = False - if end not in self.graph.nodes(): - self.graph.add_node(end) - temp_node = True - - try: - path = nx.shortest_path(self.graph, start, end) - shortest = len(path) - except nx.exception.NetworkXNoPath: - shortest = 1e6 - - if temp_node: - self.graph.remove_node(end) - return shortest - @staticmethod def snake_compass(head: dict, neck: dict) -> str: """ @@ -334,7 +297,7 @@ def is_move_safe( """ Determine if a location on the board is safe (e.g. if it's out-of-bounds or hits a different snake) or risky (e.g. if there's a chance of a head-to-head collision). Can be used in the middle of running the minimax - algorithm (but make sure to specify the "turn_over" parameter). + algorithm (but specify the "turn_over" parameter). :param move: The location of the snake's hypothetical head as a dictionary e.g. {"x": 5, "y": 10} :param snake_id: The ID of the desired snake we're evaluating a move for @@ -362,13 +325,11 @@ def is_move_safe( # Different rules apply during the middle of running minimax, depending on whose turn it is since our snake # makes moves separately from opponent snakes if turn == "ours": - # We can run into the tail of any snake since it will have to move forward + # We can run into the tail of any snake since it will have to move if move in opp_snake["body"][:-1] and snake_id != opp_id: return False, True - # We cannot hit our own head - elif move in opp_snake["body"][1:-1] and snake_id == opp_id: + elif move in opp_snake["body"][1:] and snake_id == opp_id: return False, True - # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating and length <= opp_snake["length"] # Only if the other snake is the same length or longer and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound @@ -376,18 +337,14 @@ def is_move_safe( elif turn == "opponents": if opp_id == self.my_id: - # Our snake's tail is off-limits since we will already have moved - if move in opp_snake["body"][1:]: + if move in opp_snake["body"][1:]: # Our snake's tail is off-limits since we already moved return False, True - # Avoid losing head-to-head collisions with our snake, but suicidal collisions are fine if our snake - # is the same length and also dies + # Suicidal head-to-head collisions with our snake are fine (if the lengths are the same) elif move == opp_snake["head"] and length < opp_snake["length"]: return False, True else: - # Tail is fine against other opponents - if move in opp_snake["body"][:-1]: + if move in opp_snake["body"][:-1]: # Tail is fine against other opponents return False, True - # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating and length <= opp_snake["length"] # Only if the other snake is the same length or longer and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound @@ -401,10 +358,6 @@ def is_move_safe( elif snake_id != opp_id and move == opp_snake["head"] and length <= opp_snake["length"]: return False, True - elif turn == "flood_fill": - if move in opp_snake["body"]: - return False, True - return True, risky_flag def get_obvious_moves( @@ -440,7 +393,7 @@ def get_obvious_moves( snake_id, turn="ours" if snake_id == self.my_id else "opponents" ) - if not is_safe or (risk_averse and is_risky): + if not is_safe: possible_moves.remove(move) if is_risky: risky_moves.append(move) @@ -448,8 +401,8 @@ def get_obvious_moves( if sort_by_dist_to is not None: head2 = self.all_snakes_dict[sort_by_dist_to]["head"] possible_moves = sorted(possible_moves, - key=lambda move2: self.dijkstra_shortest_path(head2, self.look_ahead(head, move2))) - if sort_by_peripheral: + key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) + elif sort_by_peripheral: possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), reverse=True) @@ -497,237 +450,45 @@ def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> return game_over, snake_still_alive - def simulate_move(self, move: str, snake_id: str, evaluate_deaths: Optional[bool] = False) -> Battlesnake: - """ - Create a new Battlesnake instance that simulates a move for a given snake - - :param move: The direction you'd like the snake to move towards (either "left", "right", "up", or "down") - :param snake_id: The ID of the desired snake we want to simulate a move for - :param evaluate_deaths: If True, remove any snakes that died as a result of the simulated move (exclusively via - head-to-head collisions) - - :return: A Battlesnake instance incorporating the simulated move in a new game state - """ - # clock_in = time.time_ns() - new_game = self.__copy__() - - # Simulate the new head position - old_head = self.all_snakes_dict[snake_id]["head"] - new_head = self.look_ahead(old_head, move) - - # Insert the simulated snake position into the new instance - new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] - new_game.all_snakes_dict[snake_id]["head"] = new_head - new_game.all_snakes_dict[snake_id]["health"] -= 1 - # Repeat for our snake's specific attributes - if snake_id == self.my_id: - new_game.my_body = [new_head] + new_game.my_body[:-1] - new_game.my_head = new_head - new_game.my_health -= 1 - - # Track if food was consumed - this elongates the snake from the tail and restores health - if new_head in self.food: - new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head - - # Check if any snakes died from this simulated move and remove them from the game - if evaluate_deaths: - # First update snake lengths from any food eaten - for update_id, snake in new_game.all_snakes_dict.items(): - if len(snake["food_eaten"]) > 0: - new_game.all_snakes_dict[update_id]["length"] += 1 - new_game.all_snakes_dict[update_id]["health"] = 100 - new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] - new_game.food = [food for food in self.food # Remove the food from the board - if not (food["x"] == snake["food_eaten"]["x"] - and food["y"] == snake["food_eaten"]["y"])] - if update_id == self.my_id: - new_game.my_length += 1 - new_game.my_health = 100 - new_game.my_body += [new_game.my_body[-1]] - # Reset the food tracker - new_game.all_snakes_dict[update_id]["food_eaten"] = [] - - # Did any snakes die from head-to-head collisions? - all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] - count_heads = Counter(all_heads) - butt_heads = [k for k, v in count_heads.items() if v > 1] # Any square where > 1 heads collided - for butt_head in butt_heads: - overlapping_snakes = np.array([ - (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() - if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) - ]) - lengths = overlapping_snakes[:, 1].astype(int) - # Special cases where the snake committed suicide and also killed our snake => don't remove - if not (self.my_id in overlapping_snakes[:, 0] - and np.count_nonzero(lengths == new_game.my_length) > 1): - indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() - if len(indices_largest_snakes) > 1: # No winner if the snakes are the same length - winner_id = None - else: - winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] - # Remove any dead snakes - for rm_id in overlapping_snakes[:, 0]: - if rm_id != winner_id: - new_game.all_snakes_dict.pop(rm_id) - - new_game.update_board() - # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return new_game - - def flood_fill( - self, - snake_id: str, - confined_area: Optional[str] = None, - risk_averse: Optional[bool] = False, - fast_forward: Optional[int] = 0 - ) -> int: - """ - Recursive function to get the total available space for a given snake. Basically, count how many £ symbols - we can fill while avoiding any $, o, and x symbols (obstacles). - - :param snake_id: The ID of the desired snake we want to do flood fill for - :param confined_area: Tells the function to do flood fill for only on one side of the snake (either "left", - "right", "up", or "down") to represent its peripheral vision - :param risk_averse: If True, flood fill will avoid any squares that directly border an opponent's head - :param fast_forward: Hypothetical scenarios where we want to see how much space we still have after moving - X turns ahead. E.g. if we set it to 5, then we remove 5 squares from all snake's tails before doing flood - fill - this is only useful when we suspect we'll be trapped by an opponent snake. - - :return: The total area of the flood fill selection - """ - head = self.all_snakes_dict[snake_id]["head"] - - if snake_id == self.my_id: # Assume we're doing flood fill for our snake - board = copy.deepcopy(self.board) - # See how flood fill changes when all snakes fast-forward X turns - if fast_forward > 0: - for snake in self.all_snakes_dict.values(): - tail_removed = snake["body"][-fast_forward:] - for remove in tail_removed: - board[remove["x"]][remove["y"]] = " " - # Try to avoid any squares that our enemy can go to - if risk_averse: - threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] - for threat in threats: - x, y = threat["x"], threat["y"] - avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in avoid_sq: - if not (n[0] == head["x"] and n[1] == head["y"]) and \ - (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): - board[n[0]][n[1]] = "x" - else: # Otherwise, generate a new board and pretend the opponent snake is our snake - board = np.full((self.board_width, self.board_height), " ") - for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): - board[square["x"], square["y"]] = "£" if num == 0 else "o" - for other_id, other_snake in self.all_snakes_dict.items(): - if other_id != snake_id: - for num, other_square in enumerate(other_snake["body"]): - board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" - - # Narrow down a portion of the board that represents the snake's peripheral vision - if confined_area is not None: - xs, ys, head = self.peripheral_vision(snake_id, confined_area) - board = board[xs[0]:xs[1], ys[0]:ys[1]] - - def fill(x, y, board, initial_square): - if board[x][y] in ["x", "$"]: # Snakes or hazards - return - if board[x][y] in ["o"]: # Our snake - return - if board[x][y] == "£" and not initial_square: # Already filled - return - board[x][y] = "£" - neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in neighbours: - if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): - fill(n[0], n[1], board, initial_square=False) - - fill(head["x"], head["y"], board, initial_square=True) - filled = sum((row == "£").sum() for row in board) - return filled - 1 if filled > 0 else filled # Exclude the head from the count, but cannot ever be negative - - def dist_to_nearest_food(self) -> int: - """ - Return the shortest distance to food for our snake, but only if we're closer to it than an opponent snake - """ - best_dist = np.inf - for food in self.food: - dist = self.dijkstra_shortest_path(food, self.my_head) - # If an enemy snake is longer than ours, and we're both 2 squares away from food, then they're technically - # closer to it since they'd win the head-to-head battle. - dist_enemy = min([self.dijkstra_shortest_path(food, snake["head"]) if snake["length"] < self.my_length - else self.dijkstra_shortest_path(food, snake["head"]) - 1 - for snake in self.opponents.values()]) - if dist < best_dist and dist_enemy >= dist: - best_dist = dist - return best_dist - def edge_kill_detection(self): """ Determine if our snake is in a position where it can get edge-killed """ - # Ignore if we're not on the edge of the board if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: return False - # possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) - # direction = self.snake_compass(self.my_head, self.my_neck) - # dir_dict = { - # "vertical": { - # "bounds": [0, self.board_width], - # "escape_dirs": ["left", "right"], - # "axis": "x", - # "axis_dir": "y", - # "scan_dir": +1 if direction == "up" else -1 - # }, - # "horizontal": { - # "bounds": [0, self.board_height], - # "escape_dirs": ["down", "up"], - # "axis": "y", - # "axis_dir": "x", - # "scan_dir": +1 if direction == "right" else -1 - # }, - # } - # dir_data = dir_dict["horizontal"] if direction in ["left", "right"] else dir_dict["vertical"] - # bounds = dir_data["bounds"] - # escape_dirs = dir_data["escape_dirs"] - # ax = dir_data["axis"] - # ax_dir = dir_data["axis_dir"] - # scan_dir = dir_data["scan_dir"] - # - # # If we can't escape (e.g. we're heading right, but can't move up or down) - # if len(set(escape_dirs).intersection(possible_moves)) == 0: - # trapped_sides = [False, False] - # for num, escape_dir in enumerate(escape_dirs): - # look = -1 if num == 0 else +1 - # # Scan the column/row to each side of us in ascending order - # if escape_dir in ["left", "right"]: - # esc_attempt = self.my_head[ax] + look - # if 0 <= esc_attempt < self.board_width: - # strip = self.board[esc_attempt, self.my_head[ax_dir]:] - # # Check if there's free space ahead of us, we're trapped if there's none - # if np.count_nonzero(strip != " ") == 0: - # trapped_sides[num] = True - # - # threats = [opp_id for opp_id, opp in self.opponents.items() - # if (opp["head"][ax] == esc_attempt) - # and (opp["head"][ax_dir] >= self.my_head[ax_dir] if look == 1 - # else opp["head"][ax_dir] <= self.my_head[ax_dir] - # )] - # - # # # Find snakes that can possibly edge-kill us - # # suspects = [opp_id for opp_id, opp in self.opponents.items() - # # if opp["head"][ax] == self.my_head[ax] + dir_data["adj_col"] and ( - # # opp["head"][dir_data["axis_dir"]] >= self.my_head[dir_data["axis_dir"]] if dir_data["scan_dir"] == 1 - # # else opp["head"][dir_data["axis_dir"]] <= self.my_head[dir_data["axis_dir"]]) - # # ] - # # if len(suspects) > 0: - # # diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspects]) - # # if diff_y == 0: - # # return True - # # gaps = [self.opponents[opp_id]["body"][-diff_y:] for opp_id in suspects] # Would leave space for us to move to - # # if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): - # # return True + possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) + direction = self.snake_compass(self.my_head, self.my_neck) + if self.my_head["x"] == 0 and "right" not in possible_moves: + # Find snakes that can possibly edge-kill us + suspect = [opp_id for opp_id, opp in self.opponents.items() + if opp["head"]["x"] == self.my_head["x"] + 1 and ( + (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) + or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) + )] + if len(suspect) > 0: + diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) + gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to + if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): + return True + if self.my_head["x"] == self.board_width - 1: + if "left" not in possible_moves: + suspect = [opp_id for opp_id, opp in self.opponents.items() + if opp["head"]["x"] == self.my_head["x"] - 1 and ( + (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) + or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) + )] + if len(suspect) > 0: + diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) + gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to + if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): + return True + # if self.my_head["y"] == 0: + # if "up" not in possible_moves: + # return True + # if self.my_head["y"] == self.board_height - 1: + # if "down" not in possible_moves: + # return True return False def heuristic(self, depth_number): @@ -758,7 +519,7 @@ def heuristic(self, depth_number): # ARE WE TRAPPED??? trap_space = None if available_space <= 10: - trap_space = available_space - self.flood_fill(self.my_id, fast_forward=available_space) + trap_space = available_space - self.flood_fill(self.my_id, estimate_moves=available_space) # Shoot we're trapped if trap_space == 0: space_penalty = -1e7 # We'd prefer getting killed than getting trapped, so penalise this more @@ -778,8 +539,7 @@ def heuristic(self, depth_number): # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): self.peripheral_size = 4 - closest_enemy = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"]))[0] - available_enemy_space = self.flood_fill(closest_enemy, confined_area="General") + available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") if available_enemy_space < 4: kill_bonus = 1000 else: @@ -790,13 +550,12 @@ def heuristic(self, depth_number): # Get closer to enemy snakes if we're longer by 3 if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): - dist_from_enemies = sorted([self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values()]) - dist_to_enemy = dist_from_enemies[0] + dist_to_enemy = self.dist_from_enemies()[0] else: dist_to_enemy = 0 # If we're getting too close to enemy snakes that are longer, RETREAT - threats = [self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] + threats = [self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] num_threats = (np.count_nonzero(np.array(threats) <= 2) * 2 + np.count_nonzero(np.array(threats) == 3) * 1) @@ -856,6 +615,151 @@ def heuristic(self, depth_number): "Threats": num_threats, "Length": self.my_length} + def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_moves=0): + """Recursive function to get the total area of the current fill selection. Basically, count how many £ symbols + we can fill while avoiding any $, O, and X symbols (obstacles). Confined_area tells the function to do flood + fill only on one side of the snake (should be either left/right/up/down).""" + head = self.all_snakes_dict[snake_id]["head"] + + # Assume we're doing flood fill for our snake + if snake_id == self.my_id: + board = copy.deepcopy(self.board) + # Let's simulate entrapment + if estimate_moves > 0: + for snake in self.all_snakes_dict.values(): + tail_removed = snake["body"][-estimate_moves:] + for remove in tail_removed: + board[remove["x"]][remove["y"]] = " " + # Let's try to avoid any squares that our enemy can go to + if risk_averse: + threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] + for threat in threats: + x, y = threat["x"], threat["y"] + avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in avoid_sq: + if not (n[0] == head["x"] and n[1] == head["y"]) and \ + (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): + board[n[0]][n[1]] = "x" + # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) + else: + board = np.full((self.board_width, self.board_height), " ") + for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): + board[square["x"], square["y"]] = "£" if num == 0 else "o" + for other_id, other_snake in self.all_snakes_dict.items(): + if other_id != snake_id: + for num, other_square in enumerate(other_snake["body"]): + board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" + + if confined_area is not None: + xs, ys, head = self.peripheral_vision(snake_id, confined_area) + board = board[xs[0]:xs[1], ys[0]:ys[1]] + # if -1 == head["x"]: + # self.peripheral_vision(snake_id, confined_area) + + def fill(x, y, board, initial_square): + if board[x][y] in ["x", "$"]: # Snakes or hazards + return + if board[x][y] in ["o"]: # Us + return + if board[x][y] == "£" and not initial_square: # Already filled + return + board[x][y] = "£" + neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in neighbours: + if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): + fill(n[0], n[1], board, initial_square=False) + + fill(head["x"], head["y"], board, initial_square=True) + filled = sum((row == "£").sum() for row in board) + return filled - 1 if filled > 0 else filled # Exclude the head, but cannot ever be negative + + def dist_to_nearest_food(self): + """Return the Manhattan distance to the nearest food for our snake""" + best_dist = np.inf + for food in self.food: + dist = self.manhattan_distance(food, self.my_head) + dist_enemy = min([self.manhattan_distance(food, snake["head"]) for snake in self.opponents.values()]) + if dist < best_dist and ((dist_enemy >= 4 and dist <= dist_enemy * 2) or (4 > dist_enemy >= dist)): + best_dist = dist + return best_dist + + def dist_from_enemies(self): + """To gauge how in danger we are, return a list of Manhattan distances to all snakes""" + return sorted([self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values()]) + + def closest_enemy(self): + """Return the nearest enemy snake""" + return sorted(self.opponents.keys(), + key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]))[0] + + def simulate_move(self, move, snake_id, evaluate_deaths=False): + """Create a new Battlesnake instance with a simulated move for a given snake""" + # clock_in = time.time_ns() + new_game = self.__copy__() + + # Simulate the new head position + old_head = self.all_snakes_dict[snake_id]["head"] + new_head = self.look_ahead(old_head, move) + + # Insert the simulated snake position into the new instance + new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] + new_game.all_snakes_dict[snake_id]["head"] = new_head + new_game.all_snakes_dict[snake_id]["health"] -= 1 + # Repeat for our snake's specific attributes + if snake_id == self.my_id: + new_game.my_body = [new_head] + new_game.my_body[:-1] + new_game.my_head = new_head + new_game.my_health -= 1 + + # Keep track if food was consumed - this elongates the snake from the tail and restores health + if new_head in self.food: + new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head + + # Check if any snakes died from this simulated move and remove them from the game + if evaluate_deaths: + # Update snake lengths from any food eaten + for update_id, snake in new_game.all_snakes_dict.items(): + if len(snake["food_eaten"]) > 0: + new_game.all_snakes_dict[update_id]["length"] += 1 + new_game.all_snakes_dict[update_id]["health"] = 100 + new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] + new_game.food = [food for food in self.food # Remove the food from the board + if not (food["x"] == snake["food_eaten"]["x"] + and food["y"] == snake["food_eaten"]["y"])] + if update_id == self.my_id: + new_game.my_length += 1 + new_game.my_health = 100 + new_game.my_body += [new_game.my_body[-1]] + # Reset the food tracker + new_game.all_snakes_dict[update_id]["food_eaten"] = [] + + # Did any snakes die from collisions? + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] + count_heads = Counter(all_heads) + butt_heads = [k for k, v in count_heads.items() if v > 1] + for butt_head in butt_heads: + overlapping_snakes = np.array([ + (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() + if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) + ]) + lengths = overlapping_snakes[:, 1].astype(int) + # Special cases where the snake committed suicide and also killed our snake => don't remove + if not (self.my_id in overlapping_snakes[:, 0] + and np.count_nonzero(lengths == new_game.my_length) > 1): + indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() + if len(indices_largest_snakes) > 1: + winner_id = None + else: + winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] + + for rm_id in overlapping_snakes[:, 0]: + if rm_id != winner_id: + new_game.all_snakes_dict.pop(rm_id) + + new_game.update_board() + # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + return new_game + @staticmethod def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data=None, insert_index=None): global tree_node_counter @@ -881,14 +785,12 @@ def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data return len(tree_nodes) - 1 def minimax(self, depth, alpha, beta, maximising_snake): - """ - Implement the minimax algorithm with alpha-beta pruning + """Implement the minimax algorithm with alpha-beta pruning :param depth: :param alpha: :param beta: :param maximising_snake: - :return: """ if depth != self.minimax_search_depth: @@ -994,7 +896,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): opp_move = ["down"] # Save time by only searching for snakes within close range - dist_opp_to_us = self.dijkstra_shortest_path(self.my_head, opp_snake["head"]) + dist_opp_to_us = self.manhattan_distance(self.my_head, opp_snake["head"]) if dist_opp_to_us <= search_within: opps_moves[opp_id] = opp_move # Put all of their moves opps_nearby += 1 @@ -1002,7 +904,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(self.opponents) <= 5: opps_moves[opp_id] = [opp_move[0]] - sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"])) + sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"])) opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_opps_by_dists.index(pair[0]))) logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " @@ -1126,3 +1028,70 @@ def optimal_move(self): plt.savefig("minimax_tree.png", bbox_inches="tight", pad_inches=0) return best_move + +# def a_star_search(self, food_loc): +# """ +# Implement the A* search algorithm to find the shortest path to food +# """ +# head_loc = self.my_head.copy() +# open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet +# closed_list = [] # Contains the nodes which are examined +# timer = 0 +# +# while len(open_list) > 0: +# if timer > 300: +# return None, None +# +# current_node = min(open_list, key=lambda node: node.f) # The node with the smallest f value +# open_list.remove(current_node) # Remove from open list +# +# if current_node.location == food_loc: # Found the food! Stop search +# traceback = [food_loc] +# pointer = current_node +# while pointer.parent is not None: +# traceback.append(pointer.parent.location) +# pointer = pointer.parent +# return traceback, current_node.g +# +# # Identify all possible nodes to move to +# neighbour_nodes = [] +# possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) +# +# for possible_move in possible_moves: +# possible_node = Node(self.look_ahead(current_node.location, possible_move), parent=current_node) +# neighbour_nodes.append(possible_node) +# +# for possible_node in neighbour_nodes: # For each possible new node +# if possible_node.location in closed_list: # Skip if the node is in the closed list +# continue +# +# # Compute f value for the neighbour node +# possible_node.g = current_node.g + 1 +# possible_node.h = self.manhattan_distance(current_node.location, food_loc) +# possible_node.f = possible_node.g + possible_node.h +# +# # If the node's location is also in the open list but has a worse g value, ignore since we can do better +# for open_node in open_list: +# if possible_node.location == open_node.location and possible_node.g > open_node.g: +# break +# +# # Otherwise, this is the best we can do so far - add to open list +# open_list.append(possible_node) +# +# closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours +# timer += 1 +# +# return None, None +# +# +class Node: + def __init__(self, location, parent=None, f=0, g=0, h=0): + self.location = location + self.neighbour_nodes = [] + self.parent = parent + self.f = f # The cost from the start node to the goal node + self.g = g # The cost from the start node to the current node + self.h = h # The cost from the current node to the goal node using a heuristic + + def __repr__(self): + return str(self.location) \ No newline at end of file From 454acf3149b604c8c98fede429f61de4647dd462 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 25 Oct 2023 13:38:12 +0100 Subject: [PATCH 44/64] Reverted to previous version yesterday New --- snake_engine.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 87aa1595..e76d63a5 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -321,7 +321,6 @@ def is_move_safe( length = self.all_snakes_dict[snake_id]["length"] risky_flag = False for opp_id, opp_snake in self.all_snakes_dict.items(): - # Different rules apply during the middle of running minimax, depending on whose turn it is since our snake # makes moves separately from opponent snakes if turn == "ours": @@ -406,6 +405,7 @@ def get_obvious_moves( possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), reverse=True) + # De-prioritise any risky moves and send them to the back if len(risky_moves) > 0: for risky in risky_moves: @@ -508,10 +508,7 @@ def heuristic(self, depth_number): # Determine available space via flood fill available_space = self.flood_fill(self.my_id, risk_averse=True) - available_space_ra = self.flood_fill(self.my_id, risk_averse=False) - if available_space_ra < 4: - space_penalty = -500 - elif available_space < 4: + if available_space < 4: space_penalty = -200 else: space_penalty = 0 @@ -639,7 +636,7 @@ def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_m for n in avoid_sq: if not (n[0] == head["x"] and n[1] == head["y"]) and \ (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): - board[n[0]][n[1]] = "x" + board[n[0]][n[1]] = "X" # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) else: board = np.full((self.board_width, self.board_height), " ") From 16b3eeacc0c59c4bda1d04c3fc24abc40d8b947a Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 25 Oct 2023 15:20:09 +0100 Subject: [PATCH 45/64] Reverted back to New --- snake_engine.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index e76d63a5..87aa1595 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -321,6 +321,7 @@ def is_move_safe( length = self.all_snakes_dict[snake_id]["length"] risky_flag = False for opp_id, opp_snake in self.all_snakes_dict.items(): + # Different rules apply during the middle of running minimax, depending on whose turn it is since our snake # makes moves separately from opponent snakes if turn == "ours": @@ -405,7 +406,6 @@ def get_obvious_moves( possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), reverse=True) - # De-prioritise any risky moves and send them to the back if len(risky_moves) > 0: for risky in risky_moves: @@ -508,7 +508,10 @@ def heuristic(self, depth_number): # Determine available space via flood fill available_space = self.flood_fill(self.my_id, risk_averse=True) - if available_space < 4: + available_space_ra = self.flood_fill(self.my_id, risk_averse=False) + if available_space_ra < 4: + space_penalty = -500 + elif available_space < 4: space_penalty = -200 else: space_penalty = 0 @@ -636,7 +639,7 @@ def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_m for n in avoid_sq: if not (n[0] == head["x"] and n[1] == head["y"]) and \ (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): - board[n[0]][n[1]] = "X" + board[n[0]][n[1]] = "x" # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) else: board = np.full((self.board_width, self.board_height), " ") From f422e6cac7946edb40fcd3d34c5fda8a7b644222 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 25 Oct 2023 16:16:20 +0100 Subject: [PATCH 46/64] Reverted back to my OG changes --- snake_engine.py | 559 +++++++++++++++++++++++++----------------------- 1 file changed, 295 insertions(+), 264 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 87aa1595..06ba7a34 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -2,6 +2,7 @@ import copy import itertools import logging +import networkx as nx import numpy as np import sys import time @@ -32,7 +33,8 @@ def __init__(self, game_state: dict, debugging: Optional[bool] = False): self.board_height = game_state["board"]["height"] self.food = game_state["board"]["food"] self.hazards = game_state["board"]["hazards"] - self.board = np.full((self.board_width, self.board_height), " ") + self.board = None # Generated later with update_board() + self.graph = None # Generated later with update_board() # Our snake's data self.my_id = game_state["you"]["id"] @@ -124,12 +126,16 @@ def update_board(self): represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner. """ self.board = np.full((self.board_width, self.board_height), " ") + self.graph = nx.grid_2d_graph(self.board_width, self.board_height) for num, my_square in enumerate(self.my_body): self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" + if num > 0: + self.graph.remove_nodes_from([(my_square["x"], my_square["y"])]) for opponent in self.opponents.values(): snake_body = opponent["body"] for num, snake_sq in enumerate(snake_body): self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "x" + self.graph.remove_nodes_from([(snake_sq["x"], snake_sq["y"])]) def display_board(self, board: Optional[np.array] = None, return_string: Optional[bool] = False): """ @@ -156,7 +162,10 @@ def display_board(self, board: Optional[np.array] = None, return_string: Optiona display_row = "" for i in range(0, len(render_board)): display_row += f"{render_board[i][-j]}| " - logging.info(display_row) + if self.debugging: + logging.info(display_row) + else: + print(display_row) # Return the board as a string instead of printing it out if return_string: @@ -195,7 +204,7 @@ def look_ahead(head: dict, move: str) -> dict: @staticmethod def manhattan_distance(start: dict, end: dict) -> int: """ - Return the Manhattan distance for two positions + Return the Manhattan distance between two positions :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} :param end: A different location on the board @@ -206,6 +215,34 @@ def manhattan_distance(start: dict, end: dict) -> int: end = (end["x"], end["y"]) return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) + def dijkstra_shortest_path(self, start: dict, end: dict) -> int: + """ + Return the shortest path between two positions using Dijkstra's algorithm implemented in networkx + + :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} + :param end: A different location on the board + + :return: The shortest distance between the start and end inputs. 1e6 if no path could be found + """ + start = (start["x"], start["y"]) + end = (end["x"], end["y"]) + + # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later + temp_node = False + if end not in self.graph.nodes(): + self.graph.add_node(end) + temp_node = True + + try: + path = nx.shortest_path(self.graph, start, end) + shortest = len(path) + except nx.exception.NetworkXNoPath: + shortest = 1e6 + + if temp_node: + self.graph.remove_node(end) + return shortest + @staticmethod def snake_compass(head: dict, neck: dict) -> str: """ @@ -297,7 +334,7 @@ def is_move_safe( """ Determine if a location on the board is safe (e.g. if it's out-of-bounds or hits a different snake) or risky (e.g. if there's a chance of a head-to-head collision). Can be used in the middle of running the minimax - algorithm (but specify the "turn_over" parameter). + algorithm (but make sure to specify the "turn_over" parameter). :param move: The location of the snake's hypothetical head as a dictionary e.g. {"x": 5, "y": 10} :param snake_id: The ID of the desired snake we're evaluating a move for @@ -325,11 +362,13 @@ def is_move_safe( # Different rules apply during the middle of running minimax, depending on whose turn it is since our snake # makes moves separately from opponent snakes if turn == "ours": - # We can run into the tail of any snake since it will have to move + # We can run into the tail of any snake since it will have to move forward if move in opp_snake["body"][:-1] and snake_id != opp_id: return False, True - elif move in opp_snake["body"][1:] and snake_id == opp_id: + # We cannot hit our own head + elif move in opp_snake["body"][1:-1] and snake_id == opp_id: return False, True + # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating and length <= opp_snake["length"] # Only if the other snake is the same length or longer and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound @@ -337,14 +376,18 @@ def is_move_safe( elif turn == "opponents": if opp_id == self.my_id: - if move in opp_snake["body"][1:]: # Our snake's tail is off-limits since we already moved + # Our snake's tail is off-limits since we will already have moved + if move in opp_snake["body"][1:]: return False, True - # Suicidal head-to-head collisions with our snake are fine (if the lengths are the same) + # Avoid losing head-to-head collisions with our snake, but suicidal collisions are fine if our snake + # is the same length and also dies elif move == opp_snake["head"] and length < opp_snake["length"]: return False, True else: - if move in opp_snake["body"][:-1]: # Tail is fine against other opponents + # Tail is fine against other opponents + if move in opp_snake["body"][:-1]: return False, True + # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating and length <= opp_snake["length"] # Only if the other snake is the same length or longer and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound @@ -358,6 +401,10 @@ def is_move_safe( elif snake_id != opp_id and move == opp_snake["head"] and length <= opp_snake["length"]: return False, True + elif turn == "flood_fill": + if move in opp_snake["body"]: + return False, True + return True, risky_flag def get_obvious_moves( @@ -393,7 +440,7 @@ def get_obvious_moves( snake_id, turn="ours" if snake_id == self.my_id else "opponents" ) - if not is_safe: + if not is_safe or (risk_averse and is_risky): possible_moves.remove(move) if is_risky: risky_moves.append(move) @@ -401,8 +448,8 @@ def get_obvious_moves( if sort_by_dist_to is not None: head2 = self.all_snakes_dict[sort_by_dist_to]["head"] possible_moves = sorted(possible_moves, - key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) - elif sort_by_peripheral: + key=lambda move2: self.dijkstra_shortest_path(head2, self.look_ahead(head, move2))) + if sort_by_peripheral: possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), reverse=True) @@ -450,45 +497,237 @@ def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> return game_over, snake_still_alive + def simulate_move(self, move: str, snake_id: str, evaluate_deaths: Optional[bool] = False) -> Battlesnake: + """ + Create a new Battlesnake instance that simulates a move for a given snake + + :param move: The direction you'd like the snake to move towards (either "left", "right", "up", or "down") + :param snake_id: The ID of the desired snake we want to simulate a move for + :param evaluate_deaths: If True, remove any snakes that died as a result of the simulated move (exclusively via + head-to-head collisions) + + :return: A Battlesnake instance incorporating the simulated move in a new game state + """ + # clock_in = time.time_ns() + new_game = self.__copy__() + + # Simulate the new head position + old_head = self.all_snakes_dict[snake_id]["head"] + new_head = self.look_ahead(old_head, move) + + # Insert the simulated snake position into the new instance + new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] + new_game.all_snakes_dict[snake_id]["head"] = new_head + new_game.all_snakes_dict[snake_id]["health"] -= 1 + # Repeat for our snake's specific attributes + if snake_id == self.my_id: + new_game.my_body = [new_head] + new_game.my_body[:-1] + new_game.my_head = new_head + new_game.my_health -= 1 + + # Track if food was consumed - this elongates the snake from the tail and restores health + if new_head in self.food: + new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head + + # Check if any snakes died from this simulated move and remove them from the game + if evaluate_deaths: + # First update snake lengths from any food eaten + for update_id, snake in new_game.all_snakes_dict.items(): + if len(snake["food_eaten"]) > 0: + new_game.all_snakes_dict[update_id]["length"] += 1 + new_game.all_snakes_dict[update_id]["health"] = 100 + new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] + new_game.food = [food for food in self.food # Remove the food from the board + if not (food["x"] == snake["food_eaten"]["x"] + and food["y"] == snake["food_eaten"]["y"])] + if update_id == self.my_id: + new_game.my_length += 1 + new_game.my_health = 100 + new_game.my_body += [new_game.my_body[-1]] + # Reset the food tracker + new_game.all_snakes_dict[update_id]["food_eaten"] = [] + + # Did any snakes die from head-to-head collisions? + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] + count_heads = Counter(all_heads) + butt_heads = [k for k, v in count_heads.items() if v > 1] # Any square where > 1 heads collided + for butt_head in butt_heads: + overlapping_snakes = np.array([ + (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() + if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) + ]) + lengths = overlapping_snakes[:, 1].astype(int) + # Special cases where the snake committed suicide and also killed our snake => don't remove + if not (self.my_id in overlapping_snakes[:, 0] + and np.count_nonzero(lengths == new_game.my_length) > 1): + indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() + if len(indices_largest_snakes) > 1: # No winner if the snakes are the same length + winner_id = None + else: + winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] + # Remove any dead snakes + for rm_id in overlapping_snakes[:, 0]: + if rm_id != winner_id: + new_game.all_snakes_dict.pop(rm_id) + + new_game.update_board() + # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + return new_game + + def flood_fill( + self, + snake_id: str, + confined_area: Optional[str] = None, + risk_averse: Optional[bool] = False, + fast_forward: Optional[int] = 0 + ) -> int: + """ + Recursive function to get the total available space for a given snake. Basically, count how many £ symbols + we can fill while avoiding any $, o, and x symbols (obstacles). + + :param snake_id: The ID of the desired snake we want to do flood fill for + :param confined_area: Tells the function to do flood fill for only on one side of the snake (either "left", + "right", "up", or "down") to represent its peripheral vision + :param risk_averse: If True, flood fill will avoid any squares that directly border an opponent's head + :param fast_forward: Hypothetical scenarios where we want to see how much space we still have after moving + X turns ahead. E.g. if we set it to 5, then we remove 5 squares from all snake's tails before doing flood + fill - this is only useful when we suspect we'll be trapped by an opponent snake. + + :return: The total area of the flood fill selection + """ + head = self.all_snakes_dict[snake_id]["head"] + + if snake_id == self.my_id: # Assume we're doing flood fill for our snake + board = copy.deepcopy(self.board) + # See how flood fill changes when all snakes fast-forward X turns + if fast_forward > 0: + for snake in self.all_snakes_dict.values(): + tail_removed = snake["body"][-fast_forward:] + for remove in tail_removed: + board[remove["x"]][remove["y"]] = " " + # Try to avoid any squares that our enemy can go to + if risk_averse: + threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] + for threat in threats: + x, y = threat["x"], threat["y"] + avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in avoid_sq: + if not (n[0] == head["x"] and n[1] == head["y"]) and \ + (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): + board[n[0]][n[1]] = "x" + else: # Otherwise, generate a new board and pretend the opponent snake is our snake + board = np.full((self.board_width, self.board_height), " ") + for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): + board[square["x"], square["y"]] = "£" if num == 0 else "o" + for other_id, other_snake in self.all_snakes_dict.items(): + if other_id != snake_id: + for num, other_square in enumerate(other_snake["body"]): + board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" + + # Narrow down a portion of the board that represents the snake's peripheral vision + if confined_area is not None: + xs, ys, head = self.peripheral_vision(snake_id, confined_area) + board = board[xs[0]:xs[1], ys[0]:ys[1]] + + def fill(x, y, board, initial_square): + if board[x][y] in ["x", "$"]: # Snakes or hazards + return + if board[x][y] in ["o"]: # Our snake + return + if board[x][y] == "£" and not initial_square: # Already filled + return + board[x][y] = "£" + neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for n in neighbours: + if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): + fill(n[0], n[1], board, initial_square=False) + + fill(head["x"], head["y"], board, initial_square=True) + filled = sum((row == "£").sum() for row in board) + return filled - 1 if filled > 0 else filled # Exclude the head from the count, but cannot ever be negative + + def dist_to_nearest_food(self) -> int: + """ + Return the shortest distance to food for our snake, but only if we're closer to it than an opponent snake + """ + best_dist = np.inf + for food in self.food: + dist = self.dijkstra_shortest_path(food, self.my_head) + # If an enemy snake is longer than ours, and we're both 2 squares away from food, then they're technically + # closer to it since they'd win the head-to-head battle. + dist_enemy = min([self.dijkstra_shortest_path(food, snake["head"]) if snake["length"] < self.my_length + else self.dijkstra_shortest_path(food, snake["head"]) - 1 + for snake in self.opponents.values()]) + if dist < best_dist and dist_enemy >= dist: + best_dist = dist + return best_dist + def edge_kill_detection(self): """ Determine if our snake is in a position where it can get edge-killed """ + # Ignore if we're not on the edge of the board if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: return False - possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) - direction = self.snake_compass(self.my_head, self.my_neck) - if self.my_head["x"] == 0 and "right" not in possible_moves: - # Find snakes that can possibly edge-kill us - suspect = [opp_id for opp_id, opp in self.opponents.items() - if opp["head"]["x"] == self.my_head["x"] + 1 and ( - (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) - or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) - )] - if len(suspect) > 0: - diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) - gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to - if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): - return True - if self.my_head["x"] == self.board_width - 1: - if "left" not in possible_moves: - suspect = [opp_id for opp_id, opp in self.opponents.items() - if opp["head"]["x"] == self.my_head["x"] - 1 and ( - (direction == "up" and opp["head"]["y"] >= self.my_head["y"]) - or (direction == "down" and opp["head"]["y"] <= self.my_head["y"]) - )] - if len(suspect) > 0: - diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspect]) - gaps = [self.opponents[snake_id]["body"][-diff_y:] for snake_id in suspect] # Would leave space for us to move to - if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): - return True - # if self.my_head["y"] == 0: - # if "up" not in possible_moves: - # return True - # if self.my_head["y"] == self.board_height - 1: - # if "down" not in possible_moves: - # return True + # possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) + # direction = self.snake_compass(self.my_head, self.my_neck) + # dir_dict = { + # "vertical": { + # "bounds": [0, self.board_width], + # "escape_dirs": ["left", "right"], + # "axis": "x", + # "axis_dir": "y", + # "scan_dir": +1 if direction == "up" else -1 + # }, + # "horizontal": { + # "bounds": [0, self.board_height], + # "escape_dirs": ["down", "up"], + # "axis": "y", + # "axis_dir": "x", + # "scan_dir": +1 if direction == "right" else -1 + # }, + # } + # dir_data = dir_dict["horizontal"] if direction in ["left", "right"] else dir_dict["vertical"] + # bounds = dir_data["bounds"] + # escape_dirs = dir_data["escape_dirs"] + # ax = dir_data["axis"] + # ax_dir = dir_data["axis_dir"] + # scan_dir = dir_data["scan_dir"] + # + # # If we can't escape (e.g. we're heading right, but can't move up or down) + # if len(set(escape_dirs).intersection(possible_moves)) == 0: + # trapped_sides = [False, False] + # for num, escape_dir in enumerate(escape_dirs): + # look = -1 if num == 0 else +1 + # # Scan the column/row to each side of us in ascending order + # if escape_dir in ["left", "right"]: + # esc_attempt = self.my_head[ax] + look + # if 0 <= esc_attempt < self.board_width: + # strip = self.board[esc_attempt, self.my_head[ax_dir]:] + # # Check if there's free space ahead of us, we're trapped if there's none + # if np.count_nonzero(strip != " ") == 0: + # trapped_sides[num] = True + # + # threats = [opp_id for opp_id, opp in self.opponents.items() + # if (opp["head"][ax] == esc_attempt) + # and (opp["head"][ax_dir] >= self.my_head[ax_dir] if look == 1 + # else opp["head"][ax_dir] <= self.my_head[ax_dir] + # )] + # + # # # Find snakes that can possibly edge-kill us + # # suspects = [opp_id for opp_id, opp in self.opponents.items() + # # if opp["head"][ax] == self.my_head[ax] + dir_data["adj_col"] and ( + # # opp["head"][dir_data["axis_dir"]] >= self.my_head[dir_data["axis_dir"]] if dir_data["scan_dir"] == 1 + # # else opp["head"][dir_data["axis_dir"]] <= self.my_head[dir_data["axis_dir"]]) + # # ] + # # if len(suspects) > 0: + # # diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspects]) + # # if diff_y == 0: + # # return True + # # gaps = [self.opponents[opp_id]["body"][-diff_y:] for opp_id in suspects] # Would leave space for us to move to + # # if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): + # # return True return False def heuristic(self, depth_number): @@ -519,7 +758,7 @@ def heuristic(self, depth_number): # ARE WE TRAPPED??? trap_space = None if available_space <= 10: - trap_space = available_space - self.flood_fill(self.my_id, estimate_moves=available_space) + trap_space = available_space - self.flood_fill(self.my_id, fast_forward=available_space) # Shoot we're trapped if trap_space == 0: space_penalty = -1e7 # We'd prefer getting killed than getting trapped, so penalise this more @@ -539,7 +778,8 @@ def heuristic(self, depth_number): # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): self.peripheral_size = 4 - available_enemy_space = self.flood_fill(self.closest_enemy(), confined_area="General") + closest_enemy = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"]))[0] + available_enemy_space = self.flood_fill(closest_enemy, confined_area="General") if available_enemy_space < 4: kill_bonus = 1000 else: @@ -550,12 +790,13 @@ def heuristic(self, depth_number): # Get closer to enemy snakes if we're longer by 3 if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): - dist_to_enemy = self.dist_from_enemies()[0] + dist_from_enemies = sorted([self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values()]) + dist_to_enemy = dist_from_enemies[0] else: dist_to_enemy = 0 # If we're getting too close to enemy snakes that are longer, RETREAT - threats = [self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] + threats = [self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] num_threats = (np.count_nonzero(np.array(threats) <= 2) * 2 + np.count_nonzero(np.array(threats) == 3) * 1) @@ -615,151 +856,6 @@ def heuristic(self, depth_number): "Threats": num_threats, "Length": self.my_length} - def flood_fill(self, snake_id, confined_area=None, risk_averse=False, estimate_moves=0): - """Recursive function to get the total area of the current fill selection. Basically, count how many £ symbols - we can fill while avoiding any $, O, and X symbols (obstacles). Confined_area tells the function to do flood - fill only on one side of the snake (should be either left/right/up/down).""" - head = self.all_snakes_dict[snake_id]["head"] - - # Assume we're doing flood fill for our snake - if snake_id == self.my_id: - board = copy.deepcopy(self.board) - # Let's simulate entrapment - if estimate_moves > 0: - for snake in self.all_snakes_dict.values(): - tail_removed = snake["body"][-estimate_moves:] - for remove in tail_removed: - board[remove["x"]][remove["y"]] = " " - # Let's try to avoid any squares that our enemy can go to - if risk_averse: - threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] - for threat in threats: - x, y = threat["x"], threat["y"] - avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in avoid_sq: - if not (n[0] == head["x"] and n[1] == head["y"]) and \ - (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): - board[n[0]][n[1]] = "x" - # Otherwise, generate a new board and pretend the opponent snake is our snake (in order to compute flood fill) - else: - board = np.full((self.board_width, self.board_height), " ") - for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): - board[square["x"], square["y"]] = "£" if num == 0 else "o" - for other_id, other_snake in self.all_snakes_dict.items(): - if other_id != snake_id: - for num, other_square in enumerate(other_snake["body"]): - board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" - - if confined_area is not None: - xs, ys, head = self.peripheral_vision(snake_id, confined_area) - board = board[xs[0]:xs[1], ys[0]:ys[1]] - # if -1 == head["x"]: - # self.peripheral_vision(snake_id, confined_area) - - def fill(x, y, board, initial_square): - if board[x][y] in ["x", "$"]: # Snakes or hazards - return - if board[x][y] in ["o"]: # Us - return - if board[x][y] == "£" and not initial_square: # Already filled - return - board[x][y] = "£" - neighbours = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] - for n in neighbours: - if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): - fill(n[0], n[1], board, initial_square=False) - - fill(head["x"], head["y"], board, initial_square=True) - filled = sum((row == "£").sum() for row in board) - return filled - 1 if filled > 0 else filled # Exclude the head, but cannot ever be negative - - def dist_to_nearest_food(self): - """Return the Manhattan distance to the nearest food for our snake""" - best_dist = np.inf - for food in self.food: - dist = self.manhattan_distance(food, self.my_head) - dist_enemy = min([self.manhattan_distance(food, snake["head"]) for snake in self.opponents.values()]) - if dist < best_dist and ((dist_enemy >= 4 and dist <= dist_enemy * 2) or (4 > dist_enemy >= dist)): - best_dist = dist - return best_dist - - def dist_from_enemies(self): - """To gauge how in danger we are, return a list of Manhattan distances to all snakes""" - return sorted([self.manhattan_distance(self.my_head, opp["head"]) for opp in self.opponents.values()]) - - def closest_enemy(self): - """Return the nearest enemy snake""" - return sorted(self.opponents.keys(), - key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]))[0] - - def simulate_move(self, move, snake_id, evaluate_deaths=False): - """Create a new Battlesnake instance with a simulated move for a given snake""" - # clock_in = time.time_ns() - new_game = self.__copy__() - - # Simulate the new head position - old_head = self.all_snakes_dict[snake_id]["head"] - new_head = self.look_ahead(old_head, move) - - # Insert the simulated snake position into the new instance - new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] - new_game.all_snakes_dict[snake_id]["head"] = new_head - new_game.all_snakes_dict[snake_id]["health"] -= 1 - # Repeat for our snake's specific attributes - if snake_id == self.my_id: - new_game.my_body = [new_head] + new_game.my_body[:-1] - new_game.my_head = new_head - new_game.my_health -= 1 - - # Keep track if food was consumed - this elongates the snake from the tail and restores health - if new_head in self.food: - new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head - - # Check if any snakes died from this simulated move and remove them from the game - if evaluate_deaths: - # Update snake lengths from any food eaten - for update_id, snake in new_game.all_snakes_dict.items(): - if len(snake["food_eaten"]) > 0: - new_game.all_snakes_dict[update_id]["length"] += 1 - new_game.all_snakes_dict[update_id]["health"] = 100 - new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] - new_game.food = [food for food in self.food # Remove the food from the board - if not (food["x"] == snake["food_eaten"]["x"] - and food["y"] == snake["food_eaten"]["y"])] - if update_id == self.my_id: - new_game.my_length += 1 - new_game.my_health = 100 - new_game.my_body += [new_game.my_body[-1]] - # Reset the food tracker - new_game.all_snakes_dict[update_id]["food_eaten"] = [] - - # Did any snakes die from collisions? - all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] - count_heads = Counter(all_heads) - butt_heads = [k for k, v in count_heads.items() if v > 1] - for butt_head in butt_heads: - overlapping_snakes = np.array([ - (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() - if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) - ]) - lengths = overlapping_snakes[:, 1].astype(int) - # Special cases where the snake committed suicide and also killed our snake => don't remove - if not (self.my_id in overlapping_snakes[:, 0] - and np.count_nonzero(lengths == new_game.my_length) > 1): - indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() - if len(indices_largest_snakes) > 1: - winner_id = None - else: - winner_id = overlapping_snakes[:, 0][indices_largest_snakes[0]] - - for rm_id in overlapping_snakes[:, 0]: - if rm_id != winner_id: - new_game.all_snakes_dict.pop(rm_id) - - new_game.update_board() - # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") - return new_game - @staticmethod def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data=None, insert_index=None): global tree_node_counter @@ -785,12 +881,14 @@ def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data return len(tree_nodes) - 1 def minimax(self, depth, alpha, beta, maximising_snake): - """Implement the minimax algorithm with alpha-beta pruning + """ + Implement the minimax algorithm with alpha-beta pruning :param depth: :param alpha: :param beta: :param maximising_snake: + :return: """ if depth != self.minimax_search_depth: @@ -896,7 +994,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): opp_move = ["down"] # Save time by only searching for snakes within close range - dist_opp_to_us = self.manhattan_distance(self.my_head, opp_snake["head"]) + dist_opp_to_us = self.dijkstra_shortest_path(self.my_head, opp_snake["head"]) if dist_opp_to_us <= search_within: opps_moves[opp_id] = opp_move # Put all of their moves opps_nearby += 1 @@ -904,7 +1002,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(self.opponents) <= 5: opps_moves[opp_id] = [opp_move[0]] - sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"])) + sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"])) opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_opps_by_dists.index(pair[0]))) logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " @@ -1027,71 +1125,4 @@ def optimal_move(self): labels=node_labels, with_labels=True, node_size=40000, font_size=20) plt.savefig("minimax_tree.png", bbox_inches="tight", pad_inches=0) - return best_move - -# def a_star_search(self, food_loc): -# """ -# Implement the A* search algorithm to find the shortest path to food -# """ -# head_loc = self.my_head.copy() -# open_list = [Node(head_loc)] # Contains the nodes that have been generated but have not been yet examined till yet -# closed_list = [] # Contains the nodes which are examined -# timer = 0 -# -# while len(open_list) > 0: -# if timer > 300: -# return None, None -# -# current_node = min(open_list, key=lambda node: node.f) # The node with the smallest f value -# open_list.remove(current_node) # Remove from open list -# -# if current_node.location == food_loc: # Found the food! Stop search -# traceback = [food_loc] -# pointer = current_node -# while pointer.parent is not None: -# traceback.append(pointer.parent.location) -# pointer = pointer.parent -# return traceback, current_node.g -# -# # Identify all possible nodes to move to -# neighbour_nodes = [] -# possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) -# -# for possible_move in possible_moves: -# possible_node = Node(self.look_ahead(current_node.location, possible_move), parent=current_node) -# neighbour_nodes.append(possible_node) -# -# for possible_node in neighbour_nodes: # For each possible new node -# if possible_node.location in closed_list: # Skip if the node is in the closed list -# continue -# -# # Compute f value for the neighbour node -# possible_node.g = current_node.g + 1 -# possible_node.h = self.manhattan_distance(current_node.location, food_loc) -# possible_node.f = possible_node.g + possible_node.h -# -# # If the node's location is also in the open list but has a worse g value, ignore since we can do better -# for open_node in open_list: -# if possible_node.location == open_node.location and possible_node.g > open_node.g: -# break -# -# # Otherwise, this is the best we can do so far - add to open list -# open_list.append(possible_node) -# -# closed_list.append(current_node.location) # Add to closed list since we've looked at all neighbours -# timer += 1 -# -# return None, None -# -# -class Node: - def __init__(self, location, parent=None, f=0, g=0, h=0): - self.location = location - self.neighbour_nodes = [] - self.parent = parent - self.f = f # The cost from the start node to the goal node - self.g = g # The cost from the start node to the current node - self.h = h # The cost from the current node to the goal node using a heuristic - - def __repr__(self): - return str(self.location) \ No newline at end of file + return best_move \ No newline at end of file From ffeda8ab7d6b7c5792e95383872ca02cf07a9d95 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Wed, 25 Oct 2023 16:34:57 +0100 Subject: [PATCH 47/64] Fixed bug with Dijkstra --- snake_engine.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 06ba7a34..dd2bc39d 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -228,18 +228,27 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: end = (end["x"], end["y"]) # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later - temp_node = False + temp_start_node = False + temp_end_node = False + if start not in self.graph.nodes(): + self.graph.add_node(start) + temp_start_node = True if end not in self.graph.nodes(): self.graph.add_node(end) - temp_node = True + temp_end_node = True + # Run networkx's Dijkstra method (it'll error out if no path is possible) try: + self.display_board() path = nx.shortest_path(self.graph, start, end) shortest = len(path) except nx.exception.NetworkXNoPath: shortest = 1e6 - if temp_node: + # Remove any nodes that were temporarily added in + if temp_start_node: + self.graph.remove_node(start) + if temp_end_node: self.graph.remove_node(end) return shortest From 75953212749fc6c3bbb8fba0ae466d6433c18b80 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Thu, 26 Oct 2023 17:29:39 +0100 Subject: [PATCH 48/64] Bug with Dijkstra vs Manhattan --- snake_engine.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index dd2bc39d..1a578784 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -127,15 +127,15 @@ def update_board(self): """ self.board = np.full((self.board_width, self.board_height), " ") self.graph = nx.grid_2d_graph(self.board_width, self.board_height) - for num, my_square in enumerate(self.my_body): - self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" - if num > 0: - self.graph.remove_nodes_from([(my_square["x"], my_square["y"])]) for opponent in self.opponents.values(): snake_body = opponent["body"] for num, snake_sq in enumerate(snake_body): self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "x" self.graph.remove_nodes_from([(snake_sq["x"], snake_sq["y"])]) + for num, my_square in enumerate(self.my_body): + self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" + if num > 0: + self.graph.remove_nodes_from([(my_square["x"], my_square["y"])]) def display_board(self, board: Optional[np.array] = None, return_string: Optional[bool] = False): """ @@ -239,7 +239,6 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: # Run networkx's Dijkstra method (it'll error out if no path is possible) try: - self.display_board() path = nx.shortest_path(self.graph, start, end) shortest = len(path) except nx.exception.NetworkXNoPath: @@ -457,7 +456,7 @@ def get_obvious_moves( if sort_by_dist_to is not None: head2 = self.all_snakes_dict[sort_by_dist_to]["head"] possible_moves = sorted(possible_moves, - key=lambda move2: self.dijkstra_shortest_path(head2, self.look_ahead(head, move2))) + key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) if sort_by_peripheral: possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), @@ -1003,7 +1002,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): opp_move = ["down"] # Save time by only searching for snakes within close range - dist_opp_to_us = self.dijkstra_shortest_path(self.my_head, opp_snake["head"]) + dist_opp_to_us = self.manhattan_distance(self.my_head, opp_snake["head"]) if dist_opp_to_us <= search_within: opps_moves[opp_id] = opp_move # Put all of their moves opps_nearby += 1 @@ -1011,7 +1010,8 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(self.opponents) <= 5: opps_moves[opp_id] = [opp_move[0]] - sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"])) + # Use Manhattan distance since Dijkstra's fails if there's no actual path + sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"])) opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_opps_by_dists.index(pair[0]))) logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " From f914259b745d5cb53c362092bdba40b80bea010b Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 30 Oct 2023 16:16:57 +0000 Subject: [PATCH 49/64] Edge kill corner cases --- snake_engine.py | 335 +++++++++++++++++++++++++++++------------------- 1 file changed, 205 insertions(+), 130 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 1a578784..f32585c0 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -18,6 +18,9 @@ tree_nodes = [] tree_node_counter = 1 +tot_time_graph = 0 +counter_graph = 0 + class Battlesnake: def __init__(self, game_state: dict, debugging: Optional[bool] = False): @@ -33,8 +36,8 @@ def __init__(self, game_state: dict, debugging: Optional[bool] = False): self.board_height = game_state["board"]["height"] self.food = game_state["board"]["food"] self.hazards = game_state["board"]["hazards"] - self.board = None # Generated later with update_board() - self.graph = None # Generated later with update_board() + self.board = np.full((self.board_width, self.board_height), " ") # Generated later with update_board() + self.graph = nx.grid_2d_graph(self.board_width, self.board_height) # Our snake's data self.my_id = game_state["you"]["id"] @@ -53,7 +56,7 @@ def __init__(self, game_state: dict, debugging: Optional[bool] = False): "body": snake["body"], "length": snake["length"], "health": snake["health"], - "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else [] + "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else None } # Weird cases when running locally where the "you" snake is not our actual snake or is empty if "name" in game_state["you"] and game_state["you"]["name"] != my_name and snake["name"] == my_name: @@ -101,7 +104,7 @@ def __copy__(self) -> Battlesnake: "body": snake["body"].copy(), "length": snake["length"], "health": snake["health"], - "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else [] + "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else None }) board = { "height": self.board_height, @@ -125,17 +128,24 @@ def update_board(self): Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner. """ - self.board = np.full((self.board_width, self.board_height), " ") - self.graph = nx.grid_2d_graph(self.board_width, self.board_height) + global tot_time_graph + global counter_graph for opponent in self.opponents.values(): snake_body = opponent["body"] for num, snake_sq in enumerate(snake_body): self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "x" + clock_in = time.time_ns() self.graph.remove_nodes_from([(snake_sq["x"], snake_sq["y"])]) + counter_graph += 1 + tot_time_graph += round((time.time_ns() - clock_in) / 1000000, 3) for num, my_square in enumerate(self.my_body): self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" if num > 0: + clock_in = time.time_ns() self.graph.remove_nodes_from([(my_square["x"], my_square["y"])]) + counter_graph += 1 + tot_time_graph += round((time.time_ns() - clock_in) / 1000000, 3) + def display_board(self, board: Optional[np.array] = None, return_string: Optional[bool] = False): """ @@ -237,6 +247,11 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: self.graph.add_node(end) temp_end_node = True + new_graph = nx.grid_2d_graph(self.board_width, self.board_height) + new_graph.add_nodes_from(sorted(self.graph.nodes())) + self.graph = new_graph + + # Run networkx's Dijkstra method (it'll error out if no path is possible) try: path = nx.shortest_path(self.graph, start, end) @@ -505,43 +520,93 @@ def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> return game_over, snake_still_alive - def simulate_move(self, move: str, snake_id: str, evaluate_deaths: Optional[bool] = False) -> Battlesnake: + def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False) -> Battlesnake: """ - Create a new Battlesnake instance that simulates a move for a given snake + Create a new Battlesnake instance that simulates a move for a given snake. To increase speed, this function + builds a new game_state dictionary from scratch to generate the new instance without affecting the original + instance. - :param move: The direction you'd like the snake to move towards (either "left", "right", "up", or "down") - :param snake_id: The ID of the desired snake we want to simulate a move for + :param move_dict: Given the ID of a desired snake, specify the direction you'd like it to move towards e.g. + {self.my_id: "left", other_snake_id: "right"} :param evaluate_deaths: If True, remove any snakes that died as a result of the simulated move (exclusively via head-to-head collisions) :return: A Battlesnake instance incorporating the simulated move in a new game state """ - # clock_in = time.time_ns() - new_game = self.__copy__() - - # Simulate the new head position - old_head = self.all_snakes_dict[snake_id]["head"] - new_head = self.look_ahead(old_head, move) - - # Insert the simulated snake position into the new instance - new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] - new_game.all_snakes_dict[snake_id]["head"] = new_head - new_game.all_snakes_dict[snake_id]["health"] -= 1 - # Repeat for our snake's specific attributes - if snake_id == self.my_id: - new_game.my_body = [new_head] + new_game.my_body[:-1] - new_game.my_head = new_head - new_game.my_health -= 1 - - # Track if food was consumed - this elongates the snake from the tail and restores health - if new_head in self.food: - new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head + # Initialise our snake's data + you = { + "id": self.my_id, + "health": self.my_health, + "body": self.my_body.copy(), + "head": self.my_head, + "length": self.my_length + } + + # Loop through all snakes and simulate a move for the relevant snake if provided + all_snakes = [] + for snake_id, snake in self.all_snakes_dict.items(): + if snake_id in move_dict: + # Update the head, body, and health of the snake to reflect the simulated move + new_head = self.look_ahead(snake["head"], move_dict[snake_id]) + all_snakes.append({ + "id": snake_id, + "head": new_head, + "body": [new_head] + snake["body"][:-1].copy(), + "length": snake["length"], + "health": snake["health"] - 1, + "food_eaten": new_head if new_head in self.food else None + }) + # Repeat for our snake's specific attributes + if snake_id == self.my_id: + you["head"] = new_head + you["body"] = [new_head] + self.my_body[:-1].copy() + you["health"] = self.my_health - 1 + else: + # No changes needed + all_snakes.append({ + "id": snake_id, + "head": snake["head"], + "body": snake["body"].copy(), + "length": snake["length"], + "health": snake["health"], + "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else None + }) + + board = { + "height": self.board_height, + "width": self.board_width, + "food": self.food.copy(), + "hazards": self.hazards.copy(), + "snakes": all_snakes + } + new_game = Battlesnake({"turn": self.turn, "board": board, "you": you}, debugging=self.debugging) + + # # clock_in = time.time_ns() + # new_game = self.__copy__() + # + # # Simulate the new head position + # old_head = self.all_snakes_dict[snake_id]["head"] + # new_head = self.look_ahead(old_head, move) + # + # # Insert the simulated snake position into the new instance + # new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] + # new_game.all_snakes_dict[snake_id]["head"] = new_head + # new_game.all_snakes_dict[snake_id]["health"] -= 1 + # # Repeat for our snake's specific attributes + # if snake_id == self.my_id: + # new_game.my_body = [new_head] + new_game.my_body[:-1] + # new_game.my_head = new_head + # new_game.my_health -= 1 + # + # # Track if food was consumed - this elongates the snake from the tail and restores health + # if new_head in self.food: + # new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head # Check if any snakes died from this simulated move and remove them from the game if evaluate_deaths: # First update snake lengths from any food eaten for update_id, snake in new_game.all_snakes_dict.items(): - if len(snake["food_eaten"]) > 0: + if snake["food_eaten"] is not None: new_game.all_snakes_dict[update_id]["length"] += 1 new_game.all_snakes_dict[update_id]["health"] = 100 new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] @@ -553,7 +618,7 @@ def simulate_move(self, move: str, snake_id: str, evaluate_deaths: Optional[bool new_game.my_health = 100 new_game.my_body += [new_game.my_body[-1]] # Reset the food tracker - new_game.all_snakes_dict[update_id]["food_eaten"] = [] + new_game.all_snakes_dict[update_id]["food_eaten"] = None # Did any snakes die from head-to-head collisions? all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] @@ -578,7 +643,6 @@ def simulate_move(self, move: str, snake_id: str, evaluate_deaths: Optional[bool if rm_id != winner_id: new_game.all_snakes_dict.pop(rm_id) - new_game.update_board() # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") return new_game @@ -587,8 +651,9 @@ def flood_fill( snake_id: str, confined_area: Optional[str] = None, risk_averse: Optional[bool] = False, - fast_forward: Optional[int] = 0 - ) -> int: + fast_forward: Optional[int] = 0, + return_touching_opps: Optional[bool] = False + ) -> int | tuple[int, list]: """ Recursive function to get the total available space for a given snake. Basically, count how many £ symbols we can fill while avoiding any $, o, and x symbols (obstacles). @@ -600,6 +665,7 @@ def flood_fill( :param fast_forward: Hypothetical scenarios where we want to see how much space we still have after moving X turns ahead. E.g. if we set it to 5, then we remove 5 squares from all snake's tails before doing flood fill - this is only useful when we suspect we'll be trapped by an opponent snake. + :param return_touching_opps: Option to return a list of other snakes whose heads our flood fill is touching :return: The total area of the flood fill selection """ @@ -638,9 +704,10 @@ def flood_fill( board = board[xs[0]:xs[1], ys[0]:ys[1]] def fill(x, y, board, initial_square): - if board[x][y] in ["x", "$"]: # Snakes or hazards + if board[x][y] == "$": # Opponent snake heads + opp_heads_in_contact.append({"x": x, "y": y}) return - if board[x][y] in ["o"]: # Our snake + if board[x][y] in ["x", "o"]: # Off-limit squares return if board[x][y] == "£" and not initial_square: # Already filled return @@ -650,9 +717,15 @@ def fill(x, y, board, initial_square): if 0 <= n[0] < len(board) and 0 <= n[1] < len(board[0]): fill(n[0], n[1], board, initial_square=False) + opp_heads_in_contact = [] fill(head["x"], head["y"], board, initial_square=True) filled = sum((row == "£").sum() for row in board) - return filled - 1 if filled > 0 else filled # Exclude the head from the count, but cannot ever be negative + flood_fill = max(filled - 1, 0) # Exclude the head from the count, but cannot ever be negative + + if return_touching_opps: + return flood_fill, opp_heads_in_contact + else: + return flood_fill def dist_to_nearest_food(self) -> int: """ @@ -674,69 +747,55 @@ def edge_kill_detection(self): """ Determine if our snake is in a position where it can get edge-killed """ - # Ignore if we're not on the edge of the board - if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: - return False - - # possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) - # direction = self.snake_compass(self.my_head, self.my_neck) - # dir_dict = { - # "vertical": { - # "bounds": [0, self.board_width], - # "escape_dirs": ["left", "right"], - # "axis": "x", - # "axis_dir": "y", - # "scan_dir": +1 if direction == "up" else -1 - # }, - # "horizontal": { - # "bounds": [0, self.board_height], - # "escape_dirs": ["down", "up"], - # "axis": "y", - # "axis_dir": "x", - # "scan_dir": +1 if direction == "right" else -1 - # }, - # } - # dir_data = dir_dict["horizontal"] if direction in ["left", "right"] else dir_dict["vertical"] - # bounds = dir_data["bounds"] - # escape_dirs = dir_data["escape_dirs"] - # ax = dir_data["axis"] - # ax_dir = dir_data["axis_dir"] - # scan_dir = dir_data["scan_dir"] - # - # # If we can't escape (e.g. we're heading right, but can't move up or down) - # if len(set(escape_dirs).intersection(possible_moves)) == 0: - # trapped_sides = [False, False] - # for num, escape_dir in enumerate(escape_dirs): - # look = -1 if num == 0 else +1 - # # Scan the column/row to each side of us in ascending order - # if escape_dir in ["left", "right"]: - # esc_attempt = self.my_head[ax] + look - # if 0 <= esc_attempt < self.board_width: - # strip = self.board[esc_attempt, self.my_head[ax_dir]:] - # # Check if there's free space ahead of us, we're trapped if there's none - # if np.count_nonzero(strip != " ") == 0: - # trapped_sides[num] = True - # - # threats = [opp_id for opp_id, opp in self.opponents.items() - # if (opp["head"][ax] == esc_attempt) - # and (opp["head"][ax_dir] >= self.my_head[ax_dir] if look == 1 - # else opp["head"][ax_dir] <= self.my_head[ax_dir] - # )] - # - # # # Find snakes that can possibly edge-kill us - # # suspects = [opp_id for opp_id, opp in self.opponents.items() - # # if opp["head"][ax] == self.my_head[ax] + dir_data["adj_col"] and ( - # # opp["head"][dir_data["axis_dir"]] >= self.my_head[dir_data["axis_dir"]] if dir_data["scan_dir"] == 1 - # # else opp["head"][dir_data["axis_dir"]] <= self.my_head[dir_data["axis_dir"]]) - # # ] - # # if len(suspects) > 0: - # # diff_y = min([self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"]) - 1 for opp_id in suspects]) - # # if diff_y == 0: - # # return True - # # gaps = [self.opponents[opp_id]["body"][-diff_y:] for opp_id in suspects] # Would leave space for us to move to - # # if self.look_ahead(self.my_head, "right") not in list(itertools.chain(*gaps)): - # # return True - return False + # # Ignore if we're not on the edge of the board + # if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: + # return False + + possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) + direction = self.snake_compass(self.my_head, self.my_neck) + dir_dict = { + "vertical": { + "bounds": [0, self.board_width], + "escape_dirs": ["left", "right"], + "axis": "x", + "axis_dir": "y", + "scan_dir": +1 if direction == "up" else -1 + }, + "horizontal": { + "bounds": [0, self.board_height], + "escape_dirs": ["down", "up"], + "axis": "y", + "axis_dir": "x", + "scan_dir": +1 if direction == "right" else -1 + }, + } + dir_data = dir_dict["horizontal"] if direction in ["left", "right"] else dir_dict["vertical"] + bounds = dir_data["bounds"] + escape_dirs = dir_data["escape_dirs"] + ax = dir_data["axis"] + ax_dir = dir_data["axis_dir"] + scan_dir = dir_data["scan_dir"] + + # If we can't escape (e.g. we're heading right, but can't move up or down) + trapped_sides = [False, False] + if len(set(escape_dirs).intersection(possible_moves)) == 0: + for num, escape_dir in enumerate(escape_dirs): + look = -1 if num == 0 else +1 + # Scan the column/row to each side of us in ascending order + if escape_dir in ["left", "right"]: + esc_attempt = self.my_head[ax] + look + if bounds[0] <= esc_attempt < bounds[1]: + # Look at the space in the column/row ahead of us + strip = self.board[esc_attempt, self.my_head[ax_dir]:] if scan_dir == +1 \ + else self.board[esc_attempt, :self.my_head[ax_dir]] + danger_strip = strip[:np.where(strip == "$")[0][0]] if "$" in strip else strip + # Check if there's free space ahead of us, we're trapped if there's none + if np.count_nonzero(danger_strip == " ") == 0: + trapped_sides[num] = True + else: + trapped_sides[num] = True + # If both sides of the snake are blocked in, then we're trapped + return True if sum(trapped_sides) == 2 else False def heuristic(self, depth_number): """Let's figure out a way to evaluate the current board for our snake :) @@ -764,18 +823,28 @@ def heuristic(self, depth_number): space_penalty = 0 # ARE WE TRAPPED??? - trap_space = None + edge_kill_check = True if available_space <= 10: - trap_space = available_space - self.flood_fill(self.my_id, fast_forward=available_space) - # Shoot we're trapped - if trap_space == 0: - space_penalty = -1e7 # We'd prefer getting killed than getting trapped, so penalise this more + fast_forward_space, opp_heads = self.flood_fill(self.my_id, fast_forward=available_space, return_touching_opps=True) + trap_space = available_space - fast_forward_space + dist_to_trapped_opp = self.dijkstra_shortest_path(self.my_head, opp_heads[0]) + trapped_opp_length = [opp_snake["length"] for opp_snake in self.opponents.values() if opp_snake["head"] == opp_heads[0]][0] + if trap_space == 0: + edge_kill_check = False + if dist_to_trapped_opp % 2 == 1 and self.my_length > trapped_opp_length: + trap_space = 1 + + # Shoot we're trapped + else: + space_penalty = -1e7 # We'd prefer getting killed than getting trapped, so penalise this more + print("WE'RE TRAPPED") - # ARE WE GOING TO GET EDGE-KILLED??? - possible_edged = self.edge_kill_detection() - if possible_edged: - space_penalty = -1e5 - self.edge_kill_detection() + else: + # ARE WE GOING TO GET EDGE-KILLED??? + possible_edged = self.edge_kill_detection() + if possible_edged: + space_penalty = -1e7 + self.edge_kill_detection() # Estimate the space we have in our peripheral vision available_peripheral = self.flood_fill(self.my_id, confined_area="auto") @@ -921,7 +990,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): return heuristic, None, heuristic_data global tree_edges - # Our snake's turn + # Minimax on our snake if maximising_snake: logging.info("=" * 50) logging.info(f"DEPTH = {depth} OUR SNAKE") @@ -938,7 +1007,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): best_val, best_move = -np.inf, None best_node_data, best_edge = None, None for num, move in enumerate(possible_moves): - SIMULATED_BOARD_INSTANCE = self.simulate_move(move, self.my_id) + SIMULATED_BOARD_INSTANCE = self.simulate_move({self.my_id: move}) logging.info(f"{len(possible_moves)} CHILD NODES: VISITING {num + 1} OF {len(possible_moves)}") logging.info(f"Running minimax for OUR SNAKE moving {move}") @@ -976,24 +1045,23 @@ def minimax(self, depth, alpha, beta, maximising_snake): logging.info(f"FINISHED MINIMAX LAYER on our snake in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") return best_val, best_move, best_node_data - # Opponents' turns + # Minimax on opponent snakes else: logging.info("=" * 50) logging.info(f"DEPTH = {depth} OPPONENT SNAKES") logging.info(f"BETA = {beta} | alpha = {alpha}") clock_in = time.time_ns() - # If there are multiple opponent snakes, search only for those within a reasonable distance of ours + + # Only simulate full set of opponent moves if they're within a reasonable distance of our snake if len(self.opponents) == 1: search_within = self.board_width * self.board_height elif len(self.opponents) <= 3: search_within = self.board_width - elif len(self.opponents) <= 5: - search_within = self.board_width // 2 + 1 else: - search_within = self.board_width // 2 + search_within = self.board_width // 2 + 1 - # Grab possible moves for all opponent snakes + # Grab all possible opponent moves opps_nearby = 0 # Counter for opponents in our vicinity opps_moves = {} # Store possible moves for each snake id for opp_id, opp_snake in self.opponents.items(): @@ -1001,18 +1069,18 @@ def minimax(self, depth, alpha, beta, maximising_snake): if len(opp_move) == 0: # If the snake has no legal moves, move down and die opp_move = ["down"] - # Save time by only searching for snakes within close range + # Save time by only using full opponent move sets if they're within a certain range dist_opp_to_us = self.manhattan_distance(self.my_head, opp_snake["head"]) if dist_opp_to_us <= search_within: opps_moves[opp_id] = opp_move # Put all of their moves opps_nearby += 1 else: - if len(self.opponents) <= 5: - opps_moves[opp_id] = [opp_move[0]] + opps_moves[opp_id] = [opp_move[0]] - # Use Manhattan distance since Dijkstra's fails if there's no actual path - sorted_opps_by_dists = sorted(self.opponents.keys(), key=lambda opp_id: self.manhattan_distance(self.my_head, self.opponents[opp_id]["head"])) - opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_opps_by_dists.index(pair[0]))) + sorted_by_dists = sorted(self.opponents.keys(), + key=lambda opp_id2: self.manhattan_distance( + self.my_head, self.opponents[opp_id2]["head"])) + opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_by_dists.index(pair[0]))) logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " f"squares of us in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") @@ -1047,13 +1115,16 @@ def minimax(self, depth, alpha, beta, maximising_snake): possible_movesets = [] possible_sims = [] # Get all possible boards by simulating moves for each opponent snake, one at a time + SIMULATED_BOARD_INSTANCE = self.__copy__() for move_combo in all_opp_combos: - SIMULATED_BOARD_INSTANCE = self.__copy__() + opp_move_dict = {} for num, move in enumerate(move_combo): - evaluate_flag = (num + 1 == len(move_combo)) - SIMULATED_BOARD_INSTANCE = SIMULATED_BOARD_INSTANCE.simulate_move( - move, list(opps_moves.keys())[num], evaluate_deaths=evaluate_flag) - possible_sims.append(SIMULATED_BOARD_INSTANCE.__copy__()) + # evaluate_flag = (num + 1 == len(move_combo)) + opp_move_dict[list(opps_moves.keys())[num]] = move + + SIMULATED_BOARD_INSTANCE2 = SIMULATED_BOARD_INSTANCE.simulate_move( + opp_move_dict, evaluate_deaths=True) + possible_sims.append(SIMULATED_BOARD_INSTANCE2) possible_movesets.append(move_combo) logging.info(f"SIMULATED {len(possible_sims)} POSSIBLE BOARDS OF OPPONENT MOVE COMBOS in " @@ -1114,6 +1185,10 @@ def optimal_move(self): tree_tracker[search_depth].append(0) _, best_move, _ = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) + print("GRAPH") + print(tot_time_graph) + print(counter_graph) + # Output a visualisation of the minimax decision tree for debugging if self.debugging: import networkx as nx From 1f3ac834cfdcf6de829e2954442cc8393adc2431 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 30 Oct 2023 17:14:57 +0000 Subject: [PATCH 50/64] fixed bug where our snake would die --- snake_engine.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index f32585c0..abf3153b 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -630,9 +630,9 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) ]) lengths = overlapping_snakes[:, 1].astype(int) + # If our snake died, don't remove it just yet # Special cases where the snake committed suicide and also killed our snake => don't remove - if not (self.my_id in overlapping_snakes[:, 0] - and np.count_nonzero(lengths == new_game.my_length) > 1): + if not (self.my_id in overlapping_snakes[:, 0]): indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() if len(indices_largest_snakes) > 1: # No winner if the snakes are the same length winner_id = None From 9fb61761c2dc2ea30235b734fe3eb3eed0fc3a47 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 30 Oct 2023 17:31:22 +0000 Subject: [PATCH 51/64] Fixed another bug --- snake_engine.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index abf3153b..cfb927c9 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -676,7 +676,8 @@ def flood_fill( # See how flood fill changes when all snakes fast-forward X turns if fast_forward > 0: for snake in self.all_snakes_dict.values(): - tail_removed = snake["body"][-fast_forward:] + to_remove = max(-(len(snake["body"]) - 1), -fast_forward) + tail_removed = snake["body"][to_remove:] for remove in tail_removed: board[remove["x"]][remove["y"]] = " " # Try to avoid any squares that our enemy can go to @@ -827,11 +828,11 @@ def heuristic(self, depth_number): if available_space <= 10: fast_forward_space, opp_heads = self.flood_fill(self.my_id, fast_forward=available_space, return_touching_opps=True) trap_space = available_space - fast_forward_space - dist_to_trapped_opp = self.dijkstra_shortest_path(self.my_head, opp_heads[0]) + dist_to_trapped_opp = self.dijkstra_shortest_path(self.my_head, opp_heads[0]) if len(opp_heads) > 0 else None trapped_opp_length = [opp_snake["length"] for opp_snake in self.opponents.values() if opp_snake["head"] == opp_heads[0]][0] if trap_space == 0: edge_kill_check = False - if dist_to_trapped_opp % 2 == 1 and self.my_length > trapped_opp_length: + if (dist_to_trapped_opp is not None and dist_to_trapped_opp % 2 == 1) and self.my_length > trapped_opp_length: trap_space = 1 # Shoot we're trapped From eeca73aa5a0ad20d41c7494dd5586a549863955a Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 30 Oct 2023 17:40:33 +0000 Subject: [PATCH 52/64] Fixed another bug with edge control --- snake_engine.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index cfb927c9..e109d0bc 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -828,15 +828,17 @@ def heuristic(self, depth_number): if available_space <= 10: fast_forward_space, opp_heads = self.flood_fill(self.my_id, fast_forward=available_space, return_touching_opps=True) trap_space = available_space - fast_forward_space - dist_to_trapped_opp = self.dijkstra_shortest_path(self.my_head, opp_heads[0]) if len(opp_heads) > 0 else None - trapped_opp_length = [opp_snake["length"] for opp_snake in self.opponents.values() if opp_snake["head"] == opp_heads[0]][0] + if len(opp_heads) > 0: + dist_to_trapped_opp = self.dijkstra_shortest_path(self.my_head, opp_heads[0]) + trapped_opp_length = [opp_snake["length"] for opp_snake in self.opponents.values() if opp_snake["head"] == opp_heads[0]][0] if trap_space == 0: - edge_kill_check = False - if (dist_to_trapped_opp is not None and dist_to_trapped_opp % 2 == 1) and self.my_length > trapped_opp_length: - trap_space = 1 + trapped = True + if len(opp_heads) > 0: + if dist_to_trapped_opp % 2 == 1 and self.my_length > trapped_opp_length: + trapped = False # Shoot we're trapped - else: + if trapped: space_penalty = -1e7 # We'd prefer getting killed than getting trapped, so penalise this more print("WE'RE TRAPPED") From 7333d733fce9f5e9cdcc629709a23dd736b6d048 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 30 Oct 2023 17:50:14 +0000 Subject: [PATCH 53/64] Fixed another bug with simulate move --- snake_engine.py | 25 ++++--------------------- 1 file changed, 4 insertions(+), 21 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index e109d0bc..42521245 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -581,27 +581,6 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False } new_game = Battlesnake({"turn": self.turn, "board": board, "you": you}, debugging=self.debugging) - # # clock_in = time.time_ns() - # new_game = self.__copy__() - # - # # Simulate the new head position - # old_head = self.all_snakes_dict[snake_id]["head"] - # new_head = self.look_ahead(old_head, move) - # - # # Insert the simulated snake position into the new instance - # new_game.all_snakes_dict[snake_id]["body"] = [new_head] + new_game.all_snakes_dict[snake_id]["body"][:-1] - # new_game.all_snakes_dict[snake_id]["head"] = new_head - # new_game.all_snakes_dict[snake_id]["health"] -= 1 - # # Repeat for our snake's specific attributes - # if snake_id == self.my_id: - # new_game.my_body = [new_head] + new_game.my_body[:-1] - # new_game.my_head = new_head - # new_game.my_health -= 1 - # - # # Track if food was consumed - this elongates the snake from the tail and restores health - # if new_head in self.food: - # new_game.all_snakes_dict[snake_id]["food_eaten"] = new_head - # Check if any snakes died from this simulated move and remove them from the game if evaluate_deaths: # First update snake lengths from any food eaten @@ -643,6 +622,10 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False if rm_id != winner_id: new_game.all_snakes_dict.pop(rm_id) + new_game.opponents = new_game.all_snakes_dict.copy() + new_game.opponents.pop(self.my_id) + new_game.update_board() + # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") return new_game From 7b4dfe5b201d41cffe5b4863ca31b887b27545a7 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Mon, 30 Oct 2023 18:43:18 +0000 Subject: [PATCH 54/64] Fixed another bug with simulate move --- snake_engine.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 42521245..30ab24c7 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -211,6 +211,12 @@ def look_ahead(head: dict, move: str) -> dict: snake_head["x"] += 1 return snake_head + def closest_distance(self, start: dict, end: dict) -> int: + closest = self.dijkstra_shortest_path(start, end) + if closest == 1e6: + closest = self.manhattan_distance(start, end) + return closest + @staticmethod def manhattan_distance(start: dict, end: dict) -> int: """ @@ -247,9 +253,9 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: self.graph.add_node(end) temp_end_node = True - new_graph = nx.grid_2d_graph(self.board_width, self.board_height) - new_graph.add_nodes_from(sorted(self.graph.nodes())) - self.graph = new_graph + # new_graph = nx.grid_2d_graph(self.board_width, self.board_height) + # new_graph.add_nodes_from(sorted(self.graph.nodes())) + # self.graph = new_graph # Run networkx's Dijkstra method (it'll error out if no path is possible) @@ -257,7 +263,7 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: path = nx.shortest_path(self.graph, start, end) shortest = len(path) except nx.exception.NetworkXNoPath: - shortest = 1e6 + shortest = 1e6 # Remove any nodes that were temporarily added in if temp_start_node: @@ -471,7 +477,7 @@ def get_obvious_moves( if sort_by_dist_to is not None: head2 = self.all_snakes_dict[sort_by_dist_to]["head"] possible_moves = sorted(possible_moves, - key=lambda move2: self.manhattan_distance(head2, self.look_ahead(head, move2))) + key=lambda move2: self.closest_distance(head2, self.look_ahead(head, move2))) if sort_by_peripheral: possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), From 02906c17b0b86b74ef516fdac5211b243449f366 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 08:47:21 +0000 Subject: [PATCH 55/64] Can stall to not die --- snake_engine.py | 68 +++++++++++++++++++++++++++++++++---------------- 1 file changed, 46 insertions(+), 22 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 30ab24c7..8a90fefc 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -242,36 +242,53 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: """ start = (start["x"], start["y"]) end = (end["x"], end["y"]) - - # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later - temp_start_node = False - temp_end_node = False - if start not in self.graph.nodes(): - self.graph.add_node(start) - temp_start_node = True - if end not in self.graph.nodes(): - self.graph.add_node(end) - temp_end_node = True - - # new_graph = nx.grid_2d_graph(self.board_width, self.board_height) - # new_graph.add_nodes_from(sorted(self.graph.nodes())) - # self.graph = new_graph - + temp_graph = self.check_missing_nodes(self.graph, [start, end]) # Run networkx's Dijkstra method (it'll error out if no path is possible) try: - path = nx.shortest_path(self.graph, start, end) + path = nx.shortest_path(temp_graph, start, end) shortest = len(path) except nx.exception.NetworkXNoPath: shortest = 1e6 - # Remove any nodes that were temporarily added in - if temp_start_node: - self.graph.remove_node(start) - if temp_end_node: - self.graph.remove_node(end) return shortest + def stall_path(self, start: dict, end: dict) -> int: + """ + Return the longest path between two positions using algorithm implemented in networkx + + :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} + :param end: A different location on the board + + :return: The longest distance between the start and end inputs. -1e6 if no path could be found + """ + start = (start["x"], start["y"]) + end = (end["x"], end["y"]) + temp_graph = self.check_missing_nodes(self.graph, [start, end]) + + find_longest = [path for path in nx.all_simple_paths(temp_graph, start, end)] + if len(find_longest) > 0: + longest_path = max(find_longest, key=lambda path: len(path)) + longest = len(longest_path) - 1 + else: + longest = -1e6 + + return longest + + @staticmethod + def check_missing_nodes(graph, nodes): + G = graph.copy() + # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later + for node in nodes: + if node not in graph.nodes(): + G.add_node(node) + x, y = node + possible_edges = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] + for e in possible_edges: + if e in G.nodes: + G.add_edge(node, e) + return G + @staticmethod def snake_compass(head: dict, neck: dict) -> str: """ @@ -814,12 +831,19 @@ def heuristic(self, depth_number): # ARE WE TRAPPED??? edge_kill_check = True - if available_space <= 10: + if available_space <= 15: fast_forward_space, opp_heads = self.flood_fill(self.my_id, fast_forward=available_space, return_touching_opps=True) trap_space = available_space - fast_forward_space + + stalling_path = self.stall_path(self.my_head, self.my_body[-available_space]) + if stalling_path < available_space: + space_penalty = -1e7 + trapped = True + if len(opp_heads) > 0: dist_to_trapped_opp = self.dijkstra_shortest_path(self.my_head, opp_heads[0]) trapped_opp_length = [opp_snake["length"] for opp_snake in self.opponents.values() if opp_snake["head"] == opp_heads[0]][0] + if trap_space == 0: trapped = True if len(opp_heads) > 0: From e1cfd06f8aa9c68fb2bc3e86e9fc0bed66e7ae76 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 09:06:45 +0000 Subject: [PATCH 56/64] Added a bug fix for networkx --- snake_engine.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index 8a90fefc..fddb06e9 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -260,7 +260,7 @@ def stall_path(self, start: dict, end: dict) -> int: :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} :param end: A different location on the board - :return: The longest distance between the start and end inputs. -1e6 if no path could be found + :return: The longest distance between the start and end inputs. 1e6 if no path could be found """ start = (start["x"], start["y"]) end = (end["x"], end["y"]) @@ -271,7 +271,7 @@ def stall_path(self, start: dict, end: dict) -> int: longest_path = max(find_longest, key=lambda path: len(path)) longest = len(longest_path) - 1 else: - longest = -1e6 + longest = 1e6 return longest @@ -831,11 +831,11 @@ def heuristic(self, depth_number): # ARE WE TRAPPED??? edge_kill_check = True - if available_space <= 15: + if available_space_ra <= 15: fast_forward_space, opp_heads = self.flood_fill(self.my_id, fast_forward=available_space, return_touching_opps=True) trap_space = available_space - fast_forward_space - - stalling_path = self.stall_path(self.my_head, self.my_body[-available_space]) + to_remove = max(-(self.my_length - 1), -available_space) + stalling_path = self.stall_path(self.my_head, self.my_body[to_remove]) if stalling_path < available_space: space_penalty = -1e7 trapped = True From c00f74c3265c3c5c1ed64cc71036ebba4b46f018 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 09:31:52 +0000 Subject: [PATCH 57/64] More time-efficient --- snake_engine.py | 35 ++++++++++++++++++++++------------- 1 file changed, 22 insertions(+), 13 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index fddb06e9..d124b188 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -20,6 +20,7 @@ tot_time_graph = 0 counter_graph = 0 +copy_graph = 0 class Battlesnake: @@ -242,7 +243,7 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: """ start = (start["x"], start["y"]) end = (end["x"], end["y"]) - temp_graph = self.check_missing_nodes(self.graph, [start, end]) + temp_graph, temp_added_nodes = self.check_missing_nodes(self.graph, [start, end]) # Run networkx's Dijkstra method (it'll error out if no path is possible) try: @@ -251,6 +252,8 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: except nx.exception.NetworkXNoPath: shortest = 1e6 + for temp_nodes in temp_added_nodes: + temp_graph.remove_node(temp_nodes) return shortest def stall_path(self, start: dict, end: dict) -> int: @@ -264,7 +267,7 @@ def stall_path(self, start: dict, end: dict) -> int: """ start = (start["x"], start["y"]) end = (end["x"], end["y"]) - temp_graph = self.check_missing_nodes(self.graph, [start, end]) + temp_graph, temp_added_nodes = self.check_missing_nodes(self.graph, [start, end]) find_longest = [path for path in nx.all_simple_paths(temp_graph, start, end)] if len(find_longest) > 0: @@ -272,22 +275,28 @@ def stall_path(self, start: dict, end: dict) -> int: longest = len(longest_path) - 1 else: longest = 1e6 - + for temp_nodes in temp_added_nodes: + temp_graph.remove_node(temp_nodes) return longest @staticmethod - def check_missing_nodes(graph, nodes): - G = graph.copy() + def check_missing_nodes(G, nodes): + global copy_graph + clock_in = time.time_ns() + # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later - for node in nodes: - if node not in graph.nodes(): + added = [] + for num, node in enumerate(nodes): + if node not in G.nodes(): + added.append(node) G.add_node(node) x, y = node possible_edges = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] for e in possible_edges: if e in G.nodes: G.add_edge(node, e) - return G + copy_graph += round((time.time_ns() - clock_in) / 1000000, 3) + return G, added @staticmethod def snake_compass(head: dict, neck: dict) -> str: @@ -1131,15 +1140,14 @@ def minimax(self, depth, alpha, beta, maximising_snake): possible_movesets = [] possible_sims = [] # Get all possible boards by simulating moves for each opponent snake, one at a time - SIMULATED_BOARD_INSTANCE = self.__copy__() + # SIMULATED_BOARD_INSTANCE = self.__copy__() for move_combo in all_opp_combos: opp_move_dict = {} for num, move in enumerate(move_combo): # evaluate_flag = (num + 1 == len(move_combo)) opp_move_dict[list(opps_moves.keys())[num]] = move - SIMULATED_BOARD_INSTANCE2 = SIMULATED_BOARD_INSTANCE.simulate_move( - opp_move_dict, evaluate_deaths=True) + SIMULATED_BOARD_INSTANCE2 = self.simulate_move(opp_move_dict, evaluate_deaths=True) possible_sims.append(SIMULATED_BOARD_INSTANCE2) possible_movesets.append(move_combo) @@ -1202,8 +1210,9 @@ def optimal_move(self): _, best_move, _ = self.minimax(depth=search_depth, alpha=-np.inf, beta=np.inf, maximising_snake=True) print("GRAPH") - print(tot_time_graph) - print(counter_graph) + print(f"Total time graphs: {tot_time_graph}") + print(f"Count of runs: {counter_graph}") + print(f"Time copying graphs: {copy_graph}") # Output a visualisation of the minimax decision tree for debugging if self.debugging: From e7cf6663f598a1586b9baf919f1eb5a062e9272c Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 10:09:22 +0000 Subject: [PATCH 58/64] Incr depth in the beginning --- snake_engine.py | 71 ++++++++++++++++++++++++++++++------------------- 1 file changed, 43 insertions(+), 28 deletions(-) diff --git a/snake_engine.py b/snake_engine.py index d124b188..9d538f4b 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -554,11 +554,11 @@ def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False) -> Battlesnake: """ - Create a new Battlesnake instance that simulates a move for a given snake. To increase speed, this function - builds a new game_state dictionary from scratch to generate the new instance without affecting the original - instance. + Create a new Battlesnake instance that simulates a game turn and makes moves for a set of desired snake IDs. To + increase speed, this function builds a new game_state dictionary from scratch to generate the new instance + without affecting the original instance. - :param move_dict: Given the ID of a desired snake, specify the direction you'd like it to move towards e.g. + :param move_dict: A dictionary containing moves that we'd like to simulate for a set of snake IDs e.g. {self.my_id: "left", other_snake_id: "right"} :param evaluate_deaths: If True, remove any snakes that died as a result of the simulated move (exclusively via head-to-head collisions) @@ -574,7 +574,7 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False "length": self.my_length } - # Loop through all snakes and simulate a move for the relevant snake if provided + # Loop through all snakes and simulate a move if provided all_snakes = [] for snake_id, snake in self.all_snakes_dict.items(): if snake_id in move_dict: @@ -594,7 +594,7 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False you["body"] = [new_head] + self.my_body[:-1].copy() you["health"] = self.my_health - 1 else: - # No changes needed + # Add the snake without any changes all_snakes.append({ "id": snake_id, "head": snake["head"], @@ -611,33 +611,48 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False "hazards": self.hazards.copy(), "snakes": all_snakes } - new_game = Battlesnake({"turn": self.turn, "board": board, "you": you}, debugging=self.debugging) # Check if any snakes died from this simulated move and remove them from the game if evaluate_deaths: # First update snake lengths from any food eaten - for update_id, snake in new_game.all_snakes_dict.items(): - if snake["food_eaten"] is not None: - new_game.all_snakes_dict[update_id]["length"] += 1 - new_game.all_snakes_dict[update_id]["health"] = 100 - new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] - new_game.food = [food for food in self.food # Remove the food from the board - if not (food["x"] == snake["food_eaten"]["x"] - and food["y"] == snake["food_eaten"]["y"])] - if update_id == self.my_id: - new_game.my_length += 1 - new_game.my_health = 100 - new_game.my_body += [new_game.my_body[-1]] + for snake_num, snake_dict in enumerate(all_snakes): + if snake_dict["food_eaten"] is not None: + all_snakes[snake_num]["length"] += 1 + all_snakes[snake_num]["health"] = 100 + all_snakes[snake_num]["body"] += [all_snakes[snake_num]["body"][-1]] + board["food"] = [food for food in board["food"] # Remove the food from the board + if not (food["x"] == snake_dict["food_eaten"]["x"] + and food["y"] == snake_dict["food_eaten"]["y"])] + if snake_dict["id"] == self.my_id: + you["length"] += 1 + you["body"] += [you["body"][-1]] + you["health"] = 100 # Reset the food tracker - new_game.all_snakes_dict[update_id]["food_eaten"] = None + all_snakes[snake_num]["food_eaten"] = None + + # for update_id, snake in new_game.all_snakes_dict.items(): + # if snake["food_eaten"] is not None: + # new_game.all_snakes_dict[update_id]["length"] += 1 + # new_game.all_snakes_dict[update_id]["health"] = 100 + # new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] + # new_game.food = [food for food in self.food # Remove the food from the board + # if not (food["x"] == snake["food_eaten"]["x"] + # and food["y"] == snake["food_eaten"]["y"])] + # if update_id == self.my_id: + # new_game.my_length += 1 + # new_game.my_health = 100 + # new_game.my_body += [new_game.my_body[-1]] + # # Reset the food tracker + # new_game.all_snakes_dict[update_id]["food_eaten"] = None # Did any snakes die from head-to-head collisions? - all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in new_game.all_snakes_dict.values()] + all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in all_snakes] count_heads = Counter(all_heads) butt_heads = [k for k, v in count_heads.items() if v > 1] # Any square where > 1 heads collided + rm_snake_indices = [] for butt_head in butt_heads: overlapping_snakes = np.array([ - (rm_id, snake["length"]) for rm_id, snake in new_game.all_snakes_dict.items() + (snake["id"], snake["length"]) for snake in all_snakes if (snake["head"]["x"] == butt_head[0] and snake["head"]["y"] == butt_head[1]) ]) lengths = overlapping_snakes[:, 1].astype(int) @@ -652,13 +667,13 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False # Remove any dead snakes for rm_id in overlapping_snakes[:, 0]: if rm_id != winner_id: - new_game.all_snakes_dict.pop(rm_id) + rm_snake_indices.extend([i for i in range(len(all_snakes)) if all_snakes[i]["id"] == rm_id]) - new_game.opponents = new_game.all_snakes_dict.copy() - new_game.opponents.pop(self.my_id) - new_game.update_board() + for i in sorted(rm_snake_indices, reverse=True): + del all_snakes[i] # logging.info(f"Done with simulation in {round((time.time_ns() - clock_in) / 1000000, 3)} ms") + new_game = Battlesnake({"turn": self.turn, "board": board, "you": you}, debugging=self.debugging) return new_game def flood_fill( @@ -1198,9 +1213,9 @@ def optimal_move(self): """Let's run the minimax algorithm with alpha-beta pruning!""" # Compute the best score of each move using the minimax algorithm with alpha-beta pruning if self.turn < 3: # Our first 3 moves are super self-explanatory tbh - search_depth = 1 + search_depth = 2 elif len(self.opponents) > 6: - search_depth = 2 # TODO should be risk-averse + search_depth = 4 # TODO should be risk-averse elif len(self.opponents) >= 4: search_depth = 4 else: From 1fb120e6c7c3fbc0937f3e11907d3fdf88b2c7d1 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 11:15:55 +0000 Subject: [PATCH 59/64] Another edge kill going down bug --- snake_engine.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/snake_engine.py b/snake_engine.py index 9d538f4b..bde0ca84 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -818,7 +818,7 @@ def edge_kill_detection(self): if bounds[0] <= esc_attempt < bounds[1]: # Look at the space in the column/row ahead of us strip = self.board[esc_attempt, self.my_head[ax_dir]:] if scan_dir == +1 \ - else self.board[esc_attempt, :self.my_head[ax_dir]] + else self.board[esc_attempt, :self.my_head[ax_dir]][::-1] danger_strip = strip[:np.where(strip == "$")[0][0]] if "$" in strip else strip # Check if there's free space ahead of us, we're trapped if there's none if np.count_nonzero(danger_strip == " ") == 0: From 6086031133678017d9ec23e9e8c1938e684f0200 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 16:17:51 +0000 Subject: [PATCH 60/64] Integrated Rick's --- battlesnake_utils | 1 + snake_engine.py | 415 ++++++++++++++++++---------------------------- 2 files changed, 166 insertions(+), 250 deletions(-) create mode 160000 battlesnake_utils diff --git a/battlesnake_utils b/battlesnake_utils new file mode 160000 index 00000000..f2735ad6 --- /dev/null +++ b/battlesnake_utils @@ -0,0 +1 @@ +Subproject commit f2735ad6ae2e3993a60e1cf02256aa066049fb6f diff --git a/snake_engine.py b/snake_engine.py index bde0ca84..e1911dcf 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -2,22 +2,24 @@ import copy import itertools import logging +import matplotlib.pyplot as plt import networkx as nx import numpy as np import sys import time +from battlesnake_utils.battlesnake_utils.battlesnake import Pos, Snake from collections import Counter -from matplotlib import colors, image as mpimg, pyplot as plt from networkx_tree import hierarchy_pos from typing import Optional my_name = "Nightwing" # Use these global variables to add data for visualising the minimax decision tree -tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} +# tree_tracker = {4: [], 3: [], 2: [], 1: [], 0: []} +tree_tracker = {6: [], 5: [], 4: [], 3: [], 2: [], 1: [], 0: []} tree_edges = [] tree_nodes = [] tree_node_counter = 1 - +# Random variables for timing runtime tot_time_graph = 0 counter_graph = 0 copy_graph = 0 @@ -35,119 +37,67 @@ def __init__(self, game_state: dict, debugging: Optional[bool] = False): self.turn = game_state["turn"] self.board_width = game_state["board"]["width"] self.board_height = game_state["board"]["height"] - self.food = game_state["board"]["food"] - self.hazards = game_state["board"]["hazards"] - self.board = np.full((self.board_width, self.board_height), " ") # Generated later with update_board() + self.food = [Pos(xy) for xy in game_state["board"]["food"]] + self.hazards = [Pos(xy) for xy in game_state["board"]["hazards"]] + self.board = np.full((self.board_width, self.board_height), " ") self.graph = nx.grid_2d_graph(self.board_width, self.board_height) - # Our snake's data - self.my_id = game_state["you"]["id"] - self.my_head = game_state["you"]["head"] - self.my_neck = game_state["you"]["body"][1] - self.my_body = game_state["you"]["body"] - self.my_length = game_state["you"]["length"] - self.my_health = game_state["you"]["health"] - - # Read snake positions as a dictionary of dictionaries (easier to access than list of dicts) - self.all_snakes_dict: dict[str, dict] = {} - for snake in game_state["board"]["snakes"]: - self.all_snakes_dict[snake["id"]] = { - "head": snake["head"], - "neck": snake["body"][1], - "body": snake["body"], - "length": snake["length"], - "health": snake["health"], - "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else None - } - # Weird cases when running locally where the "you" snake is not our actual snake or is empty - if "name" in game_state["you"] and game_state["you"]["name"] != my_name and snake["name"] == my_name: - self.my_id = snake["id"] - self.my_head = snake["head"] - self.my_neck = snake["body"][1] - self.my_body = snake["body"] - self.my_length = snake["length"] - self.my_health = snake["health"] - # Another weird edge case when running locally where our snake is not actually in the "snakes" field - if self.my_id not in self.all_snakes_dict.keys(): - self.all_snakes_dict[self.my_id] = { - "head": self.my_head, - "neck": self.my_neck, - "body": self.my_body, - "length": self.my_length, - "health": self.my_health - } + # Process our snake using Rick's Snake class + self.you = Snake(game_state["you"]) + # Process all snakes as a dictionary of Snake objects + self.all_snakes_dict: dict[str, Snake] = {} + for snake_dict in game_state["board"]["snakes"]: + self.all_snakes_dict[snake_dict["id"]] = Snake(snake_dict) # "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else None + # Weird cases when running locally where the "you" snake is not our actual snake + if game_state["you"]["name"] != my_name and snake_dict["name"] == my_name: + self.you = Snake(snake_dict) + + # Another weird edge case when running locally where our snake is not in the "snakes" field + if self.you.id not in self.all_snakes_dict.keys(): + self.all_snakes_dict[self.you.id] = self.you # Opponent snakes self.opponents = self.all_snakes_dict.copy() - self.opponents.pop(self.my_id) + self.opponents.pop(self.you.id) # Finish up our constructor self.update_board() - self.minimax_search_depth = 4 # Depth for minimax algorithm + self.minimax_search_depth = 6 # Depth for minimax algorithm self.peripheral_size = 3 # Length of our snake's "peripheral vision" self.debugging = debugging logging.basicConfig(level=logging.INFO, format="%(message)s", stream=sys.stdout) if not self.debugging: logging.disable(logging.INFO) - def __copy__(self) -> Battlesnake: - """ - Making a deep copy of the game_state dictionary takes too much time, so let's manually build it from - scratch. That way, we can modify a copied instance of the class without affecting the original instance. - - :return: A new instance of our Battlesnake class - """ - all_snakes = [] - for snake_id, snake in self.all_snakes_dict.items(): - all_snakes.append({ - "id": snake_id, - "head": snake["head"], - "body": snake["body"].copy(), - "length": snake["length"], - "health": snake["health"], - "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else None - }) - board = { - "height": self.board_height, - "width": self.board_width, - "food": self.food.copy(), - "hazards": self.hazards.copy(), - "snakes": all_snakes - } - you = { - "id": self.my_id, - "health": self.my_health, - "body": self.my_body.copy(), - "head": self.my_head, - "length": self.my_length - } - new_game_state = {"turn": self.turn, "board": board, "you": you} - return Battlesnake(new_game_state, debugging=self.debugging) - def update_board(self): """ - Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" - represents the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner. + Fill in the board with the locations of all snakes. Our snake will be displayed like "oo£" where "o" represents + the body and "£" represents the head. Opponents will be displayed as "xx£" in the same manner. + + Also update the graph representation of the board to remove nodes occupied by our snake's body (but not head), + opponent snakes, and hazards. """ global tot_time_graph global counter_graph for opponent in self.opponents.values(): - snake_body = opponent["body"] - for num, snake_sq in enumerate(snake_body): - self.board[snake_sq["x"], snake_sq["y"]] = "$" if num == 0 else "x" + opp_body = opponent.body + for num, pos in enumerate(opp_body): + self.board[pos.x, pos.y] = "$" if num == 0 else "x" clock_in = time.time_ns() - self.graph.remove_nodes_from([(snake_sq["x"], snake_sq["y"])]) + # Remove nodes on the graph occupied by opponent snakes since they shouldn't be reached + self.graph.remove_nodes_from([pos.as_tuple()]) counter_graph += 1 tot_time_graph += round((time.time_ns() - clock_in) / 1000000, 3) - for num, my_square in enumerate(self.my_body): - self.board[my_square["x"], my_square["y"]] = "£" if num == 0 else "o" + for num, pos in enumerate(self.you.body): + self.board[pos.x, pos.y] = "£" if num == 0 else "o" + # Remove nodes on the graph occupied by our snake's body, but not our head since we have to traverse the + # graph from our head if num > 0: clock_in = time.time_ns() - self.graph.remove_nodes_from([(my_square["x"], my_square["y"])]) + self.graph.remove_nodes_from([pos.as_tuple()]) counter_graph += 1 tot_time_graph += round((time.time_ns() - clock_in) / 1000000, 3) - def display_board(self, board: Optional[np.array] = None, return_string: Optional[bool] = False): """ Print out a nicely formatted board for convenient debugging e.g. @@ -191,58 +141,35 @@ def display_board(self, board: Optional[np.array] = None, return_string: Optiona board_str += display_row + "\n" return board_str - @staticmethod - def look_ahead(head: dict, move: str) -> dict: - """ - Given a possible move, return the snake's new coordinates if it were headed that way - - :param head: The location of the snake's head as a dictionary e.g. {"x": 5, "y": 10} - :param move: Either "left", "right", "up", or "down" - - :return: The new location of the snake's head if it were to move in said direction e.g. {"x": 4, "y": 10} - """ - snake_head = head.copy() - if move == "up": - snake_head["y"] += 1 - if move == "down": - snake_head["y"] -= 1 - if move == "left": - snake_head["x"] -= 1 - if move == "right": - snake_head["x"] += 1 - return snake_head - - def closest_distance(self, start: dict, end: dict) -> int: + def closest_distance(self, start: Pos, end: Pos) -> int: closest = self.dijkstra_shortest_path(start, end) if closest == 1e6: closest = self.manhattan_distance(start, end) return closest @staticmethod - def manhattan_distance(start: dict, end: dict) -> int: + def manhattan_distance(start: Pos, end: Pos) -> int: """ Return the Manhattan distance between two positions - :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} + :param start: A location on the board as a Pos object e.g. Pos({"x": 5, "y": 10}) :param end: A different location on the board :return: The Manhattan distance between the start and end inputs """ - start = (start["x"], start["y"]) - end = (end["x"], end["y"]) - return sum(abs(value1 - value2) for value1, value2 in zip(start, end)) + return sum(abs(value1 - value2) for value1, value2 in zip(start.as_tuple(), end.as_tuple())) - def dijkstra_shortest_path(self, start: dict, end: dict) -> int: + def dijkstra_shortest_path(self, start: Pos, end: Pos) -> int: """ Return the shortest path between two positions using Dijkstra's algorithm implemented in networkx - :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} + :param start: A location on the board as a Pos object e.g. Pos({"x": 5, "y": 10}) :param end: A different location on the board :return: The shortest distance between the start and end inputs. 1e6 if no path could be found """ - start = (start["x"], start["y"]) - end = (end["x"], end["y"]) + start = start.as_tuple() + end = end.as_tuple() temp_graph, temp_added_nodes = self.check_missing_nodes(self.graph, [start, end]) # Run networkx's Dijkstra method (it'll error out if no path is possible) @@ -256,17 +183,17 @@ def dijkstra_shortest_path(self, start: dict, end: dict) -> int: temp_graph.remove_node(temp_nodes) return shortest - def stall_path(self, start: dict, end: dict) -> int: + def stall_path(self, start: Pos, end: Pos) -> int: """ Return the longest path between two positions using algorithm implemented in networkx - :param start: A location on the board as a dictionary e.g. {"x": 5, "y": 10} + :param start: A location on the board as a Pos object e.g. Pos({"x": 5, "y": 10}) :param end: A different location on the board :return: The longest distance between the start and end inputs. 1e6 if no path could be found """ - start = (start["x"], start["y"]) - end = (end["x"], end["y"]) + start = start.as_tuple() + end = end.as_tuple() temp_graph, temp_added_nodes = self.check_missing_nodes(self.graph, [start, end]) find_longest = [path for path in nx.all_simple_paths(temp_graph, start, end)] @@ -280,17 +207,17 @@ def stall_path(self, start: dict, end: dict) -> int: return longest @staticmethod - def check_missing_nodes(G, nodes): + def check_missing_nodes(G, nodes: list[tuple]): global copy_graph clock_in = time.time_ns() - - # If the desired location is on a hazard or snake, then it's absent from the graph - add it in but remove later + # If the desired location is on a hazard/snake, then it's absent from the graph, and we want to add in the node added = [] for num, node in enumerate(nodes): if node not in G.nodes(): added.append(node) G.add_node(node) x, y = node + # Include edges to connect the added node to surrounding nodes if possible possible_edges = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] for e in possible_edges: if e in G.nodes: @@ -347,8 +274,8 @@ def peripheral_vision(self, snake_id: str, direction: str) -> tuple[tuple, tuple flood-fill on the peripheral field) """ # Our peripheral field of vision when scanning for moves - head = self.all_snakes_dict[snake_id]["head"].copy() - neck = self.all_snakes_dict[snake_id]["neck"].copy() + head = self.all_snakes_dict[snake_id].head.as_dict() + neck = self.all_snakes_dict[snake_id].body_dict[1] dim = self.peripheral_size # Got to figure out the direction ourselves @@ -378,11 +305,11 @@ def peripheral_vision(self, snake_id: str, direction: str) -> tuple[tuple, tuple peripheral_box_y = max(head["y"] - dim, 0), min(head["y"] + dim + 1, self.board_height) head["x"], head["y"] = head["x"] - peripheral_box_x[0], head["y"] - peripheral_box_y[0] - return peripheral_box_x, peripheral_box_y, head + return peripheral_box_x, peripheral_box_y, Pos(head) def is_move_safe( self, - move: dict, + move: Pos, snake_id: str, turn: Optional[str] = "done" ) -> tuple[bool, bool]: @@ -391,7 +318,7 @@ def is_move_safe( (e.g. if there's a chance of a head-to-head collision). Can be used in the middle of running the minimax algorithm (but make sure to specify the "turn_over" parameter). - :param move: The location of the snake's hypothetical head as a dictionary e.g. {"x": 5, "y": 10} + :param move: The location of the snake's hypothetical head as a Pos object e.g. Pos({"x": 5, "y": 10}) :param snake_id: The ID of the desired snake we're evaluating a move for :param turn: Either "ours", "opponents", or "done". Addresses nuances with running this function during the minimax algorithm or outside of it. If "ours", this means we're at a depth where our snake has to make a @@ -404,13 +331,13 @@ def is_move_safe( True if the square is risky, False otherwise """ # Prevent snake from moving out of bounds - if move["x"] < 0 or move["x"] >= self.board_width: + if move.x < 0 or move.x >= self.board_width: return False, True - if move["y"] < 0 or move["y"] >= self.board_height: + if move.y < 0 or move.y >= self.board_height: return False, True # Prevent snake from colliding with other snakes - length = self.all_snakes_dict[snake_id]["length"] + length = self.all_snakes_dict[snake_id].length risky_flag = False for opp_id, opp_snake in self.all_snakes_dict.items(): @@ -418,46 +345,46 @@ def is_move_safe( # makes moves separately from opponent snakes if turn == "ours": # We can run into the tail of any snake since it will have to move forward - if move in opp_snake["body"][:-1] and snake_id != opp_id: + if move in opp_snake.body[:-1] and snake_id != opp_id: return False, True # We cannot hit our own head - elif move in opp_snake["body"][1:-1] and snake_id == opp_id: + elif move in opp_snake.body[1:-1] and snake_id == opp_id: return False, True # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating - and length <= opp_snake["length"] # Only if the other snake is the same length or longer - and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound + and length <= opp_snake.length # Only if the other snake is the same length or longer + and self.manhattan_distance(move, opp_snake.head) == 1): # Only if we're collision-bound risky_flag = True elif turn == "opponents": - if opp_id == self.my_id: + if opp_id == self.you.id: # Our snake's tail is off-limits since we will already have moved - if move in opp_snake["body"][1:]: + if move in opp_snake.body[1:]: return False, True # Avoid losing head-to-head collisions with our snake, but suicidal collisions are fine if our snake # is the same length and also dies - elif move == opp_snake["head"] and length < opp_snake["length"]: + elif move == opp_snake.head and length < opp_snake.length: return False, True else: # Tail is fine against other opponents - if move in opp_snake["body"][:-1]: + if move in opp_snake.body[:-1]: return False, True # Flag a move as risky if it could lead to a losing head-to-head collision elif (snake_id != opp_id # Skip the same snake we're evaluating - and length <= opp_snake["length"] # Only if the other snake is the same length or longer - and self.manhattan_distance(move, opp_snake["head"]) == 1): # Only if we're collision-bound + and length <= opp_snake.length # Only if the other snake is the same length or longer + and self.manhattan_distance(move, opp_snake.head) == 1): # Only if we're collision-bound risky_flag = True elif turn == "done": # Move is invalid if it collides with the body of a snake - if move in opp_snake["body"][1:]: + if move in opp_snake.body[1:]: return False, True # Move is invalid if it collides with the head of a snake that is the same length or longer - elif snake_id != opp_id and move == opp_snake["head"] and length <= opp_snake["length"]: + elif snake_id != opp_id and move == opp_snake.head and length <= opp_snake.length: return False, True elif turn == "flood_fill": - if move in opp_snake["body"]: + if move in opp_snake.body: return False, True return True, risky_flag @@ -488,12 +415,12 @@ def get_obvious_moves( # Loop through possible moves and remove from consideration if it's invalid possible_moves = ["up", "down", "left", "right"] risky_moves = [] - head = self.all_snakes_dict[snake_id]["head"] + head = self.all_snakes_dict[snake_id].head for move in possible_moves.copy(): is_safe, is_risky = self.is_move_safe( - self.look_ahead(head, move), + head.moved_to(move), snake_id, - turn="ours" if snake_id == self.my_id else "opponents" + turn="ours" if snake_id == self.you.id else "opponents" ) if not is_safe or (risk_averse and is_risky): possible_moves.remove(move) @@ -501,9 +428,9 @@ def get_obvious_moves( risky_moves.append(move) if sort_by_dist_to is not None: - head2 = self.all_snakes_dict[sort_by_dist_to]["head"] + head2 = self.all_snakes_dict[sort_by_dist_to].head possible_moves = sorted(possible_moves, - key=lambda move2: self.closest_distance(head2, self.look_ahead(head, move2))) + key=lambda move2: self.closest_distance(head2, head.moved_to(move2))) if sort_by_peripheral: possible_moves = sorted(possible_moves, key=lambda move2: self.flood_fill(snake_id, confined_area=move2), @@ -539,16 +466,16 @@ def is_game_over(self, for_snake_id: str | list, depth: Optional[int] = None) -> snake_monitor = {} # A dictionary for each snake showing whether they're alive for snake_id, snake in self.all_snakes_dict.items(): # Check if each snake's head is in a safe square, depending on if we're at a depth where only we made a move - is_safe, _ = self.is_move_safe(snake["head"], snake_id, turn="done" if depth % 2 == 0 else "ours") + is_safe, _ = self.is_move_safe(snake.head, snake_id, turn="done" if depth % 2 == 0 else "ours") snake_monitor[snake_id] = is_safe # Game is over if there's only one snake remaining or if our snake died - game_over = True if (sum(snake_monitor.values()) == 1 or not snake_monitor[self.my_id]) else False + game_over = True if (sum(snake_monitor.values()) == 1 or not snake_monitor[self.you.id]) else False # See if a specific snake is alive or not if isinstance(for_snake_id, (list, tuple)): snake_still_alive = [snake_monitor[snake_id] for snake_id in for_snake_id] else: - snake_still_alive = snake_monitor[self.my_id] if for_snake_id is None else snake_monitor[for_snake_id] + snake_still_alive = snake_monitor[self.you.id] if for_snake_id is None else snake_monitor[for_snake_id] return game_over, snake_still_alive @@ -559,7 +486,7 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False without affecting the original instance. :param move_dict: A dictionary containing moves that we'd like to simulate for a set of snake IDs e.g. - {self.my_id: "left", other_snake_id: "right"} + {self.you.id: "left", other_snake_id: "right"} :param evaluate_deaths: If True, remove any snakes that died as a result of the simulated move (exclusively via head-to-head collisions) @@ -567,11 +494,12 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False """ # Initialise our snake's data you = { - "id": self.my_id, - "health": self.my_health, - "body": self.my_body.copy(), - "head": self.my_head, - "length": self.my_length + "id": self.you.id, + "name": self.you.name, + "health": self.you.health, + "body": self.you.body_dict, + "head": self.you.head.as_dict(), + "length": self.you.length } # Loop through all snakes and simulate a move if provided @@ -579,35 +507,37 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False for snake_id, snake in self.all_snakes_dict.items(): if snake_id in move_dict: # Update the head, body, and health of the snake to reflect the simulated move - new_head = self.look_ahead(snake["head"], move_dict[snake_id]) + new_head: dict = snake.head.moved_to(move_dict[snake_id]).as_dict() all_snakes.append({ "id": snake_id, + "name": snake.name, "head": new_head, - "body": [new_head] + snake["body"][:-1].copy(), - "length": snake["length"], - "health": snake["health"] - 1, - "food_eaten": new_head if new_head in self.food else None + "body": [new_head] + snake.body_dict[:-1], + "length": snake.length, + "health": snake.health - 1, + "food_eaten": new_head if Pos(new_head) in self.food else None }) # Repeat for our snake's specific attributes - if snake_id == self.my_id: + if snake_id == self.you.id: you["head"] = new_head - you["body"] = [new_head] + self.my_body[:-1].copy() - you["health"] = self.my_health - 1 + you["body"] = [new_head] + self.you.body_dict[:-1] + you["health"] = self.you.health - 1 else: # Add the snake without any changes all_snakes.append({ "id": snake_id, - "head": snake["head"], - "body": snake["body"].copy(), - "length": snake["length"], - "health": snake["health"], - "food_eaten": snake["food_eaten"] if "food_eaten" in snake.keys() else None + "name": snake.name, + "head": snake.head.as_dict(), + "body": snake.body_dict, + "length": snake.length, + "health": snake.health, + "food_eaten": getattr(snake, "food_eaten", None) }) board = { "height": self.board_height, "width": self.board_width, - "food": self.food.copy(), + "food": [xy.as_dict() for xy in self.food], "hazards": self.hazards.copy(), "snakes": all_snakes } @@ -623,28 +553,13 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False board["food"] = [food for food in board["food"] # Remove the food from the board if not (food["x"] == snake_dict["food_eaten"]["x"] and food["y"] == snake_dict["food_eaten"]["y"])] - if snake_dict["id"] == self.my_id: + if snake_dict["id"] == self.you.id: you["length"] += 1 you["body"] += [you["body"][-1]] you["health"] = 100 # Reset the food tracker all_snakes[snake_num]["food_eaten"] = None - # for update_id, snake in new_game.all_snakes_dict.items(): - # if snake["food_eaten"] is not None: - # new_game.all_snakes_dict[update_id]["length"] += 1 - # new_game.all_snakes_dict[update_id]["health"] = 100 - # new_game.all_snakes_dict[update_id]["body"] += [new_game.all_snakes_dict[update_id]["body"][-1]] - # new_game.food = [food for food in self.food # Remove the food from the board - # if not (food["x"] == snake["food_eaten"]["x"] - # and food["y"] == snake["food_eaten"]["y"])] - # if update_id == self.my_id: - # new_game.my_length += 1 - # new_game.my_health = 100 - # new_game.my_body += [new_game.my_body[-1]] - # # Reset the food tracker - # new_game.all_snakes_dict[update_id]["food_eaten"] = None - # Did any snakes die from head-to-head collisions? all_heads = [(snake["head"]["x"], snake["head"]["y"]) for snake in all_snakes] count_heads = Counter(all_heads) @@ -658,7 +573,7 @@ def simulate_move(self, move_dict: dict, evaluate_deaths: Optional[bool] = False lengths = overlapping_snakes[:, 1].astype(int) # If our snake died, don't remove it just yet # Special cases where the snake committed suicide and also killed our snake => don't remove - if not (self.my_id in overlapping_snakes[:, 0]): + if not (self.you.id in overlapping_snakes[:, 0]): indices_largest_snakes = np.argwhere(lengths == lengths.max()).flatten().tolist() if len(indices_largest_snakes) > 1: # No winner if the snakes are the same length winner_id = None @@ -699,35 +614,35 @@ def flood_fill( :return: The total area of the flood fill selection """ - head = self.all_snakes_dict[snake_id]["head"] + head = self.all_snakes_dict[snake_id].head - if snake_id == self.my_id: # Assume we're doing flood fill for our snake + if snake_id == self.you.id: # Assume we're doing flood fill for our snake board = copy.deepcopy(self.board) # See how flood fill changes when all snakes fast-forward X turns if fast_forward > 0: for snake in self.all_snakes_dict.values(): - to_remove = max(-(len(snake["body"]) - 1), -fast_forward) - tail_removed = snake["body"][to_remove:] + to_remove = max(-(len(snake.body) - 1), -fast_forward) + tail_removed = snake.body[to_remove:] for remove in tail_removed: - board[remove["x"]][remove["y"]] = " " + board[remove.x][remove.y] = " " # Try to avoid any squares that our enemy can go to if risk_averse: - threats = [other["head"] for other in self.opponents.values() if other["length"] >= self.my_length] + threats = [other.head for other in self.opponents.values() if other.length >= self.you.length] for threat in threats: - x, y = threat["x"], threat["y"] + x, y = threat.x, threat.y avoid_sq = [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)] for n in avoid_sq: - if not (n[0] == head["x"] and n[1] == head["y"]) and \ + if not (n[0] == head.x and n[1] == head.y) and \ (0 <= n[0] < self.board_width and 0 <= n[1] < self.board_height): board[n[0]][n[1]] = "x" else: # Otherwise, generate a new board and pretend the opponent snake is our snake board = np.full((self.board_width, self.board_height), " ") - for num, square in enumerate(self.all_snakes_dict[snake_id]["body"]): - board[square["x"], square["y"]] = "£" if num == 0 else "o" + for num, square in enumerate(self.all_snakes_dict[snake_id].body): + board[square.x, square.y] = "£" if num == 0 else "o" for other_id, other_snake in self.all_snakes_dict.items(): if other_id != snake_id: - for num, other_square in enumerate(other_snake["body"]): - board[other_square["x"], other_square["y"]] = "$" if num == 0 else "x" + for num, other_square in enumerate(other_snake.body): + board[other_square.x, other_square.y] = "$" if num == 0 else "x" # Narrow down a portion of the board that represents the snake's peripheral vision if confined_area is not None: @@ -736,7 +651,7 @@ def flood_fill( def fill(x, y, board, initial_square): if board[x][y] == "$": # Opponent snake heads - opp_heads_in_contact.append({"x": x, "y": y}) + opp_heads_in_contact.append(Pos(x, y)) return if board[x][y] in ["x", "o"]: # Off-limit squares return @@ -749,7 +664,7 @@ def fill(x, y, board, initial_square): fill(n[0], n[1], board, initial_square=False) opp_heads_in_contact = [] - fill(head["x"], head["y"], board, initial_square=True) + fill(head.x, head.y, board, initial_square=True) filled = sum((row == "£").sum() for row in board) flood_fill = max(filled - 1, 0) # Exclude the head from the count, but cannot ever be negative @@ -764,11 +679,11 @@ def dist_to_nearest_food(self) -> int: """ best_dist = np.inf for food in self.food: - dist = self.dijkstra_shortest_path(food, self.my_head) + dist = self.dijkstra_shortest_path(food, self.you.head) # If an enemy snake is longer than ours, and we're both 2 squares away from food, then they're technically # closer to it since they'd win the head-to-head battle. - dist_enemy = min([self.dijkstra_shortest_path(food, snake["head"]) if snake["length"] < self.my_length - else self.dijkstra_shortest_path(food, snake["head"]) - 1 + dist_enemy = min([self.dijkstra_shortest_path(food, snake.head) if snake.length < self.you.length + else self.dijkstra_shortest_path(food, snake.head) - 1 for snake in self.opponents.values()]) if dist < best_dist and dist_enemy >= dist: best_dist = dist @@ -782,8 +697,8 @@ def edge_kill_detection(self): # if 0 < self.my_head["x"] < self.board_width - 1 and 0 < self.my_head["y"] < self.board_height - 1: # return False - possible_moves = self.get_obvious_moves(self.my_id, risk_averse=True) - direction = self.snake_compass(self.my_head, self.my_neck) + possible_moves = self.get_obvious_moves(self.you.id, risk_averse=True) + direction = self.you.facing_direction() dir_dict = { "vertical": { "bounds": [0, self.board_width], @@ -814,11 +729,11 @@ def edge_kill_detection(self): look = -1 if num == 0 else +1 # Scan the column/row to each side of us in ascending order if escape_dir in ["left", "right"]: - esc_attempt = self.my_head[ax] + look + esc_attempt = self.you.head.as_dict()[ax] + look if bounds[0] <= esc_attempt < bounds[1]: # Look at the space in the column/row ahead of us - strip = self.board[esc_attempt, self.my_head[ax_dir]:] if scan_dir == +1 \ - else self.board[esc_attempt, :self.my_head[ax_dir]][::-1] + strip = self.board[esc_attempt, self.you.head.as_dict()[ax_dir]:] if scan_dir == +1 \ + else self.board[esc_attempt, :self.you.head.as_dict()[ax_dir]][::-1] danger_strip = strip[:np.where(strip == "$")[0][0]] if "$" in strip else strip # Check if there's free space ahead of us, we're trapped if there's none if np.count_nonzero(danger_strip == " ") == 0: @@ -828,14 +743,15 @@ def edge_kill_detection(self): # If both sides of the snake are blocked in, then we're trapped return True if sum(trapped_sides) == 2 else False - def heuristic(self, depth_number): - """Let's figure out a way to evaluate the current board for our snake :) + def heuristic(self, depth_number: int) -> tuple[float, dict]: + """ + Evaluate the current board for our snake (the higher, the better) - Should be a function of: - - Maximising available space for our snake (flood fill) - - Minimising available space for enemy snakes - - Distance to food - - How far down our game tree we went in our minimax search + :param depth_number: The current depth our snake is at in the minimax search tree + + :return: + The computed heuristic value + A dictionary of select metrics to be used for debugging """ # Determine how many layers deep in the game tree we are layers_deep = self.minimax_search_depth - depth_number @@ -844,8 +760,8 @@ def heuristic(self, depth_number): opponents_left = len(self.opponents) # Determine available space via flood fill - available_space = self.flood_fill(self.my_id, risk_averse=True) - available_space_ra = self.flood_fill(self.my_id, risk_averse=False) + available_space = self.flood_fill(self.you.id, risk_averse=True) + available_space_ra = self.flood_fill(self.you.id, risk_averse=False) if available_space_ra < 4: space_penalty = -500 elif available_space < 4: @@ -856,22 +772,22 @@ def heuristic(self, depth_number): # ARE WE TRAPPED??? edge_kill_check = True if available_space_ra <= 15: - fast_forward_space, opp_heads = self.flood_fill(self.my_id, fast_forward=available_space, return_touching_opps=True) + fast_forward_space, opp_heads = self.flood_fill(self.you.id, fast_forward=available_space, return_touching_opps=True) trap_space = available_space - fast_forward_space - to_remove = max(-(self.my_length - 1), -available_space) - stalling_path = self.stall_path(self.my_head, self.my_body[to_remove]) + to_remove = max(-(self.you.length - 1), -available_space) + stalling_path = self.stall_path(self.you.head, self.you.body[to_remove]) if stalling_path < available_space: space_penalty = -1e7 trapped = True if len(opp_heads) > 0: - dist_to_trapped_opp = self.dijkstra_shortest_path(self.my_head, opp_heads[0]) - trapped_opp_length = [opp_snake["length"] for opp_snake in self.opponents.values() if opp_snake["head"] == opp_heads[0]][0] + dist_to_trapped_opp = self.dijkstra_shortest_path(self.you.head, opp_heads[0]) + trapped_opp_length = [opp_snake.length for opp_snake in self.opponents.values() if opp_snake.head == opp_heads[0]][0] if trap_space == 0: trapped = True if len(opp_heads) > 0: - if dist_to_trapped_opp % 2 == 1 and self.my_length > trapped_opp_length: + if dist_to_trapped_opp % 2 == 1 and self.you.length > trapped_opp_length: trapped = False # Shoot we're trapped @@ -887,15 +803,15 @@ def heuristic(self, depth_number): self.edge_kill_detection() # Estimate the space we have in our peripheral vision - available_peripheral = self.flood_fill(self.my_id, confined_area="auto") + available_peripheral = self.flood_fill(self.you.id, confined_area="auto") # We want to minimise available space for our opponents via flood fill (but only when there are fewer snakes in # our vicinity) if len(self.opponents) <= 3: # and sum([dist < (self.board_width // 2) for dist in self.dist_from_enemies()]) <= 3 \ - # and len(self.opponents) == sum([self.my_length > s["length"] for s in self.opponents.values()]): + # and len(self.opponents) == sum([self.you.length > s["length"] for s in self.opponents.values()]): self.peripheral_size = 4 - closest_enemy = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.my_head, self.opponents[opp_id]["head"]))[0] + closest_enemy = sorted(self.opponents.keys(), key=lambda opp_id: self.dijkstra_shortest_path(self.you.head, self.opponents[opp_id].head))[0] available_enemy_space = self.flood_fill(closest_enemy, confined_area="General") if available_enemy_space < 4: kill_bonus = 1000 @@ -906,26 +822,26 @@ def heuristic(self, depth_number): kill_bonus = 0 # Get closer to enemy snakes if we're longer by 3 - if 2 >= len(self.opponents) == sum([self.my_length > s["length"] + 3 for s in self.opponents.values()]): - dist_from_enemies = sorted([self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values()]) + if 2 >= len(self.opponents) == sum([self.you.length > s.length + 3 for s in self.opponents.values()]): + dist_from_enemies = sorted([self.dijkstra_shortest_path(self.you.head.as_dict(), opp.head) for opp in self.opponents.values()]) dist_to_enemy = dist_from_enemies[0] else: dist_to_enemy = 0 # If we're getting too close to enemy snakes that are longer, RETREAT - threats = [self.dijkstra_shortest_path(self.my_head, opp["head"]) for opp in self.opponents.values() if opp["length"] >= self.my_length] + threats = [self.dijkstra_shortest_path(self.you.head, opp.head) for opp in self.opponents.values() if opp.length >= self.you.length] num_threats = (np.count_nonzero(np.array(threats) <= 2) * 2 + np.count_nonzero(np.array(threats) == 3) * 1) # Determine the closest safe distance to food dist_food = self.dist_to_nearest_food() - health_flag = True if self.my_health < 40 else False - shortest_flag = True if sum([self.my_length <= snake["length"] for snake in self.opponents.values()]) >= min([2, len(self.opponents)]) else False - longest_flag = True if sum([self.my_length > snake["length"] for snake in self.opponents.values()]) == len(self.opponents) else False + health_flag = True if self.you.health < 40 else False + shortest_flag = True if sum([self.you.length <= snake.length for snake in self.opponents.values()]) >= min([2, len(self.opponents)]) else False + longest_flag = True if sum([self.you.length > snake.length for snake in self.opponents.values()]) == len(self.opponents) else False # Are we in the centre of the board? Maximise control centre = range(self.board_width // 2 - 2, self.board_width // 2 + 3) - in_centre = (self.my_head["x"] in centre and self.my_head["x"] in centre) and (len(self.opponents) <= 2) + in_centre = (self.you.head.as_dict()["x"] in centre and self.you.head.as_dict()["x"] in centre) and (len(self.opponents) <= 2) # Heuristic formula space_weight = 1 @@ -949,7 +865,7 @@ def heuristic(self, depth_number): logging.info(f"Available enemy space: {available_enemy_space}") logging.info(f"Kill bonus: {kill_bonus}") logging.info(f"In centre: {in_centre}") - logging.info(f"Length: {self.my_length}") + logging.info(f"Length: {self.you.length}") h = (available_space * space_weight) + space_penalty + \ (peripheral_weight * available_peripheral) + \ @@ -957,12 +873,11 @@ def heuristic(self, depth_number): (threat_proximity_weight * num_threats) + \ (food_weight / (dist_food + 1)) + \ (layers_deep * depth_weight) + \ - (self.my_length * length_weight) + \ + (self.you.length * length_weight) + \ in_centre * centre_control_weight + \ aggression_weight / (dist_to_enemy + 1) + \ (enemy_restriction_weight / (available_enemy_space + 1)) + kill_bonus - return h, {"Heur": round(h, 2), "Space": available_space, "Penalty": space_penalty, @@ -971,7 +886,7 @@ def heuristic(self, depth_number): "Enemy Dist": dist_to_enemy, "Enemy Kill": available_enemy_space + kill_bonus, "Threats": num_threats, - "Length": self.my_length} + "Length": self.you.length} @staticmethod def update_tree_visualisation(depth, add_edges=False, add_nodes=False, node_data=None, insert_index=None): @@ -1010,7 +925,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): """ if depth != self.minimax_search_depth: # Check if our snake died - game_over, still_alive = self.is_game_over(for_snake_id=self.my_id, depth=depth) + game_over, still_alive = self.is_game_over(for_snake_id=self.you.id, depth=depth) if not still_alive: logging.info("Our snake died...") heuristic = -1e6 + (self.minimax_search_depth - depth) # Reward slower deaths @@ -1038,16 +953,16 @@ def minimax(self, depth, alpha, beta, maximising_snake): clock_in = time.time_ns() possible_moves = self.get_obvious_moves( # If > 6 opponents, we'll do depth = 2 and risk_averse = True - self.my_id, risk_averse=(len(self.opponents) > 6), sort_by_peripheral=True) + self.you.id, risk_averse=(len(self.opponents) > 6), sort_by_peripheral=True) if len(possible_moves) == 0 and len(self.opponents) > 6: # Try again, but do any risky move - possible_moves = self.get_obvious_moves(self.my_id, risk_averse=False, sort_by_peripheral=True) + possible_moves = self.get_obvious_moves(self.you.id, risk_averse=False, sort_by_peripheral=True) if len(possible_moves) == 0: # RIP possible_moves = ["down"] best_val, best_move = -np.inf, None best_node_data, best_edge = None, None for num, move in enumerate(possible_moves): - SIMULATED_BOARD_INSTANCE = self.simulate_move({self.my_id: move}) + SIMULATED_BOARD_INSTANCE = self.simulate_move({self.you.id: move}) logging.info(f"{len(possible_moves)} CHILD NODES: VISITING {num + 1} OF {len(possible_moves)}") logging.info(f"Running minimax for OUR SNAKE moving {move}") @@ -1105,12 +1020,12 @@ def minimax(self, depth, alpha, beta, maximising_snake): opps_nearby = 0 # Counter for opponents in our vicinity opps_moves = {} # Store possible moves for each snake id for opp_id, opp_snake in self.opponents.items(): - opp_move = self.get_obvious_moves(opp_id, risk_averse=False, sort_by_dist_to=self.my_id) + opp_move = self.get_obvious_moves(opp_id, risk_averse=False, sort_by_dist_to=self.you.id) if len(opp_move) == 0: # If the snake has no legal moves, move down and die opp_move = ["down"] # Save time by only using full opponent move sets if they're within a certain range - dist_opp_to_us = self.manhattan_distance(self.my_head, opp_snake["head"]) + dist_opp_to_us = self.manhattan_distance(self.you.head, opp_snake.head) if dist_opp_to_us <= search_within: opps_moves[opp_id] = opp_move # Put all of their moves opps_nearby += 1 @@ -1119,7 +1034,7 @@ def minimax(self, depth, alpha, beta, maximising_snake): sorted_by_dists = sorted(self.opponents.keys(), key=lambda opp_id2: self.manhattan_distance( - self.my_head, self.opponents[opp_id2]["head"])) + self.you.head, self.opponents[opp_id2].head)) opps_moves = dict(sorted(opps_moves.items(), key=lambda pair: sorted_by_dists.index(pair[0]))) logging.info(f"Found {opps_nearby} of {len(self.opponents)} OPPONENT SNAKES within {search_within} " From ef3809bf93936eec54fcac0382db0e768112ffa7 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 16:36:46 +0000 Subject: [PATCH 61/64] Fixing depth = 6 --- snake_engine.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/snake_engine.py b/snake_engine.py index e1911dcf..493f4e75 100644 --- a/snake_engine.py +++ b/snake_engine.py @@ -823,7 +823,7 @@ def heuristic(self, depth_number: int) -> tuple[float, dict]: # Get closer to enemy snakes if we're longer by 3 if 2 >= len(self.opponents) == sum([self.you.length > s.length + 3 for s in self.opponents.values()]): - dist_from_enemies = sorted([self.dijkstra_shortest_path(self.you.head.as_dict(), opp.head) for opp in self.opponents.values()]) + dist_from_enemies = sorted([self.dijkstra_shortest_path(self.you.head, opp.head) for opp in self.opponents.values()]) dist_to_enemy = dist_from_enemies[0] else: dist_to_enemy = 0 From 92fd19a48bce49cb09b6152a5846fd86c0900551 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 17:14:39 +0000 Subject: [PATCH 62/64] test --- battlesnake_utils | 1 - 1 file changed, 1 deletion(-) delete mode 160000 battlesnake_utils diff --git a/battlesnake_utils b/battlesnake_utils deleted file mode 160000 index f2735ad6..00000000 --- a/battlesnake_utils +++ /dev/null @@ -1 +0,0 @@ -Subproject commit f2735ad6ae2e3993a60e1cf02256aa066049fb6f From b8e2c64337b73ac5684453039222e29fcd0d4286 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 17:18:19 +0000 Subject: [PATCH 63/64] FORK --- .gitmodules | 3 +++ battlesnake-utils | 1 + 2 files changed, 4 insertions(+) create mode 100644 .gitmodules create mode 160000 battlesnake-utils diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 00000000..1035a489 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "battlesnake-utils"] + path = battlesnake-utils + url = https://github.com/Diluent67/battlesnake-utils.git diff --git a/battlesnake-utils b/battlesnake-utils new file mode 160000 index 00000000..9a3af2f4 --- /dev/null +++ b/battlesnake-utils @@ -0,0 +1 @@ +Subproject commit 9a3af2f47ce5f5bf3da81c9d7c5e49809593d90c From 51333b05ef4e4b19baa8dd62fadf0ad073337d50 Mon Sep 17 00:00:00 2001 From: G Tonthat Date: Tue, 31 Oct 2023 17:24:42 +0000 Subject: [PATCH 64/64] MEH --- .gitmodules | 3 --- battlesnake-utils | 1 - 2 files changed, 4 deletions(-) delete mode 160000 battlesnake-utils diff --git a/.gitmodules b/.gitmodules index 1035a489..e69de29b 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +0,0 @@ -[submodule "battlesnake-utils"] - path = battlesnake-utils - url = https://github.com/Diluent67/battlesnake-utils.git diff --git a/battlesnake-utils b/battlesnake-utils deleted file mode 160000 index 9a3af2f4..00000000 --- a/battlesnake-utils +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 9a3af2f47ce5f5bf3da81c9d7c5e49809593d90c

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