Support parallelized world

docs/Coordinator.md

@@ -2,16 +2,29 @@
 Coordinator is the centerpiece of the game orchestration. It provides an interface between the agents and the AIDojo world.
 
 1. Registration of new agents in the game
-2. Verification of agents' actionf format
+2. Verification of agents' action format
 3. Recording (and storing) trajectories of agents
 4. Detection of episode ends (either by reaching timout or agents reaching their respective goals)
 5. Assigning rewards for each action and at the end of each episode
 6. Removing agents from the game
 7. Registering the GameReset requests and handelling the game resets.
 
+## Connction to other game components
+Coordinator, having the role of the middle man in all communication between the agent and the world uses several queues for massing passing and handelling.
+
+1. `Actions queue` is a queue in which the agents submit their actions. It provides N:1 communication channel in which the coordinator receives the inputs.
+2. `Answer queue` is a separeate queue **per agent** in which the results of the actions are send to the agent.
+3.  `World action queue` is a queue used for sending the acions from coordinator to the AI Dojo world
+4. `World response queue` is a channel used for wolrd -> coordinator communicaiton (responses to the agents' action)
+<img src="/docs/figures/message_passing_coordinator.jpg" alt="Message passing overview" width="30%"/>
+
+
 ## Main components of the coordinator
-`self._actions_queue`: asycnio queue for agent -> aidojo_world communication
-`self._answers_queue`: asycnio queue for aidojo_world -> agent communication
+`self._actions_queue`: asycnio queue for agents -> coordinator communication
+`self._answer_queues`: dictionary of asycnio queues for coordinator -> agent communication (1 queue per agent)
+`self._world_action_queue`: asycnio queue for coordinator -> world  queue communication
+`self._world_response_queue`: asycnio queue for world -> coordinator  queue communication
+`self.task_config`: Object with the configuration of the scenario
 `self.ALLOWED_ROLES`: list of allowed agent roles [`Attacker`, `Defender`, `Benign`]
 `self._world`: Instance of `AIDojoWorld`. Implements the dynamics of the world   
 `self._CONFIG_FILE_HASH`: hash of the configuration file used in the interaction (scenario, topology, etc.). Used for better reproducibility of results
@@ -24,33 +37,11 @@ Coordinator is the centerpiece of the game orchestration. It provides an interfa
 ### Agent information components
 `self.agents`: information about connected agents {`agent address`: (`agent_name`,`agent_role`)}
 `self._agent_steps`: step counter for each agent in the current episode
-`self._reset_requests`: dictionary where requests for episode reset are collected (the world resets only if ALL agents request reset)
+`self._reset_requests`: dictionary where requests for episode reset are collected (the world resets only if **all** active agents request reset)
 `self._agent_observations`: current observation per agent
 `self._agent_starting_position`: starting position (with wildcards, see [configuration](../README.md#task-configuration)) per agent
 `self._agent_states`: current GameState per agent 
-`self._agent_statuses`: status of each agent. One of following options:
-    - `playing`: agent is registered and can participate in current episode. Can't influence the episode termination
-    - `playing_active`: agent is registered and can participate in current episode. It has `goal` and `max_steps` defined and can influence the termination of the episode
-    - `goal_reached`: agent has reached it's goal in this episode. It can't perform any more actions until the interaction is resetted.
-    - `blocked`: agent has been blocked. It can't perform any more actions until the interaction is resetted.
-    - `max_steps`: agent has reached it's maximum allowed steps. It can't perform any more actions until the interaction is resetted.
-
-
+`self._agent_last_action`: last Action per agent
+`self._agent_statuses`: status of each agent. One of AgentStatus
 `self._agent_rewards`: dictionary of final reward of each agent in the current episod. Only agent's which can't participate in the ongoing episode are listed.
-`self._agent_trajectories`: complete trajectories for each agent in the ongoing episode
-
-## The format of the messages to the agents is
-    {
-    "to_agent": address of client, 
-    "status": {
-        "#players": number of players,
-        "running": true or false,
-        "time": time in game,
-        } ,
-    "message": Generic text messages (optional),
-    "state": (optional) {
-        "observation": observation_object,
-        "ended": if the game ended or not,
-        "reason": reason for ending
-    }
-    }
+`self._agent_trajectories`: complete trajectories for each agent in the ongoing episode

env/game_components.py

@@ -460,7 +460,9 @@ def from_json(cls, json_string):
 @enum.unique
 class GameStatus(enum.Enum):
     OK = 200
+
     CREATED = 201
+    RESET_DONE = 202
     BAD_REQUEST = 400
     FORBIDDEN = 403
 
@@ -475,6 +477,8 @@ def from_string(cls, string:str):
                 return GameStatus.BAD_REQUEST
             case "GameStatus.FORBIDDEN":
                 return GameStatus.FORBIDDEN
+            case "GameStatus.RESET_DONE":
+                return GameStatus.RESET_DONE
     def __repr__(self) -> str:
         return str(self)
 if __name__ == "__main__":

env/worlds/aidojo_world.py

@@ -2,29 +2,37 @@
 # Template of world to be used in AI Dojo
 import sys
 import os
+import asyncio
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-import env.game_components as components
 import logging
 from utils.utils import ConfigParser
+from env.game_components import GameState, Action, GameStatus, ActionType
 
 """
 Basic class for worlds to be used in the AI Dojo.
 Every world (environment) used in AI Dojo should extend this class and implement
 all its methods to be compatible with the game server and game coordinator.
 """
 class AIDojoWorld(object):
-    def __init__(self, task_config_file:str, world_name:str="BasicAIDojoWorld")->None:
+    def __init__(self, task_config_file:str,action_queue:asyncio.Queue, response_queue:asyncio.Queue, world_name:str="BasicAIDojoWorld")->None:
         self.task_config = ConfigParser(task_config_file)
         self.logger = logging.getLogger(world_name)
+        self._action_queue = action_queue
+        self._response_queue = response_queue
+        self._world_name = world_name
+
+    @property
+    def world_name(self)->str:
+        return self._world_name
 
-    def step(self, current_state:components.GameState, action:components.Action, agent_id:tuple)-> components.GameState:
+    def step(self, current_state:GameState, action:Action, agent_id:tuple)-> GameState:
         """
         Executes given action in a current state of the environment and produces new GameState.
         """
         raise NotImplementedError
 
-    def create_state_from_view(self, view:dict, add_neighboring_nets:bool=True)->components.GameState:
+    def create_state_from_view(self, view:dict, add_neighboring_nets:bool=True)->GameState:
         """
         Produces a GameState based on the view of the world.
         """
@@ -46,4 +54,31 @@ def update_goal_dict(self, goal_dict:dict)->dict:
         """
         Takes the existing goal dict and updates it with respect to the world.
         """
-        raise NotImplementedError
+        raise NotImplementedError
+
+    async def handle_incoming_action(self)->None:
+        try:
+            self.logger.info(f"\tStaring {self.world_name} task.")
+            while True:
+                agent_id, action, game_state = await self._action_queue.get()
+                self.logger.debug(f"Received from{agent_id}: {action}, {game_state}.")
+                match action.type:
+                    case ActionType.JoinGame:
+                        msg = (agent_id, (self.create_state_from_view(game_state), GameStatus.CREATED))
+                    case ActionType.QuitGame:
+                        msg = (agent_id, (GameState(),GameStatus.OK))
+                    case ActionType.ResetGame:
+                        if agent_id == "world": #reset the world
+                            self.reset()
+                            continue
+                        else:
+                            msg = (agent_id, (self.create_state_from_view(game_state), GameStatus.RESET_DONE))
+                    case _:
+                        new_state = self.step(game_state, action,agent_id)
+                        msg = (agent_id, (new_state, GameStatus.OK))
+                # new_state = self.step(state, action, agent_id)
+                self.logger.debug(f"Sending to{agent_id}: {msg}")
+                await self._response_queue.put(msg)
+                await asyncio.sleep(0)
+        except asyncio.CancelledError:
+            self.logger.info(f"\t{self.world_name} Terminating by CancelledError")