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‎README.md‎

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@@ -24,6 +24,8 @@ pip3 install git+https://github.com/pvnetto/URNAI-Tools/
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The basic installation will install all the *basic* required dependencies, including OpenAI Gym and SC2LE. But for other supported environments, you will need to install them for yourself. We describe how to do this on the next section.
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To use tensorflowp-cpu instead of gpu, go to Optional below.
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### Optional
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#### Starcraft II
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#### VizDoom
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Before setting urnai to install vizdoom, please see if you have all dependencies installed.
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Go [here] first.(https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md#deps)
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To install urnai with vizdoom support, use:
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- On Unix:
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```
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set "URNAI_DEEPRTS=1" && set "URNAI_VIZDOOM=1" && set "URNAI_2048=1" && pip3 install urnai
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```
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#### Tensorflow CPU
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By default, urnai depdens on tensorflow-gpu, to use tf-cpu instead, use:
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- On Unix:
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```
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URNAI_TF_CPU=1 pip3 install urnai
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```
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- On Windows:
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```
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set "URNAI_TF_CPU=1" && pip3 install urnai
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```
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### Running the examples
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To execute any of the examples we've included, just navigate to the project's folder and run them using Python.
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From version 0.0.2+ you can use json-files:
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```
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git clone https://github.com/marcocspc/URNAI-Tools
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cd 'URNAI-Tools/urnai/test/solves'
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urnai train --json-file=solve_x.json
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```
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## Command line
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You can now use urnai on command line. Commands:
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To see what you can do, use:
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```
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cd 'project/save/path'
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python solve_x.py
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urnai -h
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```
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## Guide
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## Building your own code
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Follow these instructions to start developing new stuff using our library.
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@@ -139,7 +167,6 @@ Here you'll find all the things that we plan to do in this project. **Bold** ite
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* [X] Frozenlake
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* [X] Cartpole-V0
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* [X] Cartpole-V1
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* [X] Taxi-V2
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* [ ] Flappy Bird
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* [ ] **StarCraft II - Simple 64 Map - Very Easy difficulty**
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‎urnai/agents/rewards/vizdoom.py‎

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POSITION_Y = 15
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POSITION_Z = 16
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GENERAL_REWARD = 17
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METHOD_CUMULATIVE = "cumulative"
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METHOD_DIFFERENCE = "difference"
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METHOD_POSITIVE_ONLY = "positive_only"
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def __init__(self, method):
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self.method = method
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def get_reward(self, obs, reward, done):
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r = 0
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#r += -10 * obs.game_variables[VizDoomHealthGatheringReward.DEAD]
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#r += 15 * obs.game_variables[VizDoomHealthGatheringReward.ITEMCOUNT]
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r += obs.game_variables[VizDoomHealthGatheringReward.HEALTH] - self.prev_health
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#r += -10 * reward
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if r >0: r *= 5
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self.prev_health = obs.game_variables[VizDoomHealthGatheringReward.HEALTH]
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if method == VizDoomHealthGatheringReward.METHOD_CUMULATIVE:
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r += obs.game_variables[VizDoomHealthGatheringReward.HEALTH]
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elif method == VizDoomHealthGatheringReward.METHOD_DIFFERENCE:
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r += obs.game_variables[VizDoomHealthGatheringReward.HEALTH] - self.prev_health
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self.prev_health = obs.game_variables[VizDoomHealthGatheringReward.HEALTH]
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elif method == VizDoomHealthGatheringReward.METHOD_POSITIVE_ONLY:
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r += obs.game_variables[VizDoomHealthGatheringReward.HEALTH] - self.prev_health
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self.prev_health = obs.game_variables[VizDoomHealthGatheringReward.HEALTH]
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if r < 0: r = 0
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return r
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‎urnai/test/solves/solve_vizdoom.json‎

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},
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"reward" : {
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"class" : "VizDoomHealthGatheringReward",
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"params" : {}
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},
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"agent" : {
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"class" : "GenericAgent",
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"params" : {}
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},
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"trainer" : {
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"class" : "Trainer",
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"params" : {
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"file_name" : "vizdoom_jsontrainer_test",
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"save_every" : "100",
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"enable_save" : true
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}
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},
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"json_trainer" : {
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"train": {
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"num_episodes" : 3000,
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"reward_from_env" : true,
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"max_steps" : 500
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},
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"play" : {
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"num_matches" : 100
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}
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}
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},
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{
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"env" : {
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"class" : "VizdoomEnv",
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"params" : {
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"wad" : "/Users/marcocspc/git/URNAI-Tools/urnai/utils/vizdoomwads/health_gathering.wad",
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"render" : true,
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"doommap" : null
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}
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},
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"action-wrapper" : {
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"class" : "VizdoomHealthGatheringWrapper",
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"params" : {}
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},
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"state_builder" : {
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"class" : "VizDoomHealthGatheringState",
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"params" : {
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"screen_width" : 160,
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"screen_height" : 120
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}
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},
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"model" : {
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"class" : "DQNKerasMem",
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"params" : {
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"learning_rate" : 0.005,
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"gamma" : 0.9,
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"use_memory" : false,
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"per_episode_epsilon_decay" : true,
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"build_model" : [
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{
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"type": "conv",
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"filters": 32,
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"filter_shape": [3, 3],
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"padding": "same",
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"name": "default0",
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"input_shape": [120, 160, 1],
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"max_pooling_pool_size_shape": [2, 2]
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},
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{
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"type": "conv",
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"filters": 32,
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"filter_shape": [3, 3],
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"padding": "same",
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"name": "default1",
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"input_shape": null,
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"max_pooling_pool_size_shape": [2, 2]
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},
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{
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"type": "fullyconn",
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"nodes": 256,
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"name": "default2"
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},
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{
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"type": "fullyconn",
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"nodes": 50,
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"name": "default3"
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},
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{
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"type": "output",
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"length": 3
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}
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]
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}
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},
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"reward" : {
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"class" : "VizDoomHealthGatheringReward",
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"params" : {}
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"method" : "cumulative"
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}
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},
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"agent" : {
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"class" : "GenericAgent",
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"params" : {}
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"params" : {}
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},
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"trainer" : {
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"class" : "Trainer",

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