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Musha-the-Yusha
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poca-SoccerTwos

Reinforcement Learning
ml-agents
TensorBoard
ONNX
unity-ml-agents
deep-reinforcement-learning
ML-Agents-SoccerTwos
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  1. README.md +31 -0
  2. SoccerTwos.onnx +3 -0
  3. SoccerTwos/SoccerTwos-0.onnx +3 -0
  4. SoccerTwos/SoccerTwos-0.pt +3 -0
  5. SoccerTwos/SoccerTwos-10413793.pt +3 -0
  6. SoccerTwos/SoccerTwos-168.onnx +3 -0
  7. SoccerTwos/SoccerTwos-168.pt +3 -0
  8. SoccerTwos/SoccerTwos-20530.onnx +3 -0
  9. SoccerTwos/SoccerTwos-20530.pt +3 -0
  10. SoccerTwos/SoccerTwos-2921228.pt +3 -0
  11. SoccerTwos/SoccerTwos-4248774.pt +3 -0
  12. SoccerTwos/SoccerTwos-548.onnx +3 -0
  13. SoccerTwos/SoccerTwos-548.pt +3 -0
  14. SoccerTwos/SoccerTwos-7999985.onnx +3 -0
  15. SoccerTwos/SoccerTwos-7999985.pt +3 -0
  16. SoccerTwos/SoccerTwos-8292104.pt +3 -0
  17. SoccerTwos/SoccerTwos-8499885.onnx +3 -0
  18. SoccerTwos/SoccerTwos-8499885.pt +3 -0
  19. SoccerTwos/SoccerTwos-8999827.onnx +3 -0
  20. SoccerTwos/SoccerTwos-8999827.pt +3 -0
  21. SoccerTwos/SoccerTwos-9499944.onnx +3 -0
  22. SoccerTwos/SoccerTwos-9499944.pt +3 -0
  23. SoccerTwos/SoccerTwos-9999995.onnx +3 -0
  24. SoccerTwos/SoccerTwos-9999995.pt +3 -0
  25. SoccerTwos/checkpoint.pt +3 -0
  26. SoccerTwos/events.out.tfevents.1676303152.deus-Katana.48500.0 +3 -0
  27. SoccerTwos/events.out.tfevents.1676313932.deus-Katana.71759.0 +3 -0
  28. SoccerTwos/events.out.tfevents.1676324785.deus-Katana.13826.0 +3 -0
  29. SoccerTwos/events.out.tfevents.1676368417.deus-Katana.21597.0 +3 -0
  30. SoccerTwos/events.out.tfevents.1676368905.deus-Katana.4514.0 +3 -0
  31. SoccerTwos/events.out.tfevents.1676415314.deus-Katana.9746.0 +3 -0
  32. SoccerTwos/events.out.tfevents.1676450256.deus-Katana.5096.0 +3 -0
  33. SoccerTwos/events.out.tfevents.1676453670.deus-Katana.10470.0 +3 -0
  34. SoccerTwos/events.out.tfevents.1676453768.deus-Katana.3990.0 +3 -0
  35. config.json +1 -0
  36. configuration.yaml +82 -0
  37. run_logs/Player-0.log +226 -0
  38. run_logs/timers.json +49 -0
  39. run_logs/training_status.json +66 -0
README.md ADDED
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+
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+ ---
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+ tags:
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+ - unity-ml-agents
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+ - ml-agents
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - ML-Agents-SoccerTwos
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+ library_name: ml-agents
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+ ---
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+
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+ # **poca** Agent playing **SoccerTwos**
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+ This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
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+
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+ ## Usage (with ML-Agents)
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+ The Documentation: https://github.com/huggingface/ml-agents#get-started
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+ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
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+
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+
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+ ### Resume the training
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+ ```
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+ mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
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+ ```
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+ ### Watch your Agent play
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+ You can watch your agent **playing directly in your browser:**.
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+
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+ 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
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+ 2. Step 1: Write your model_id: Musha-the-Yusha/poca-SoccerTwos
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+ 3. Step 2: Select your *.nn /*.onnx file
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+ 4. Click on Watch the agent play 👀
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+
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+ {"default_settings": null, "behaviors": {"SoccerTwos": {"trainer_type": "poca", "hyperparameters": {"batch_size": 512, "buffer_size": 20480, "learning_rate": 0.0003, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 5, "learning_rate_schedule": "constant", "beta_schedule": "constant", "epsilon_schedule": "constant"}, "checkpoint_interval": 500000, "network_settings": {"normalize": false, "hidden_units": 512, "num_layers": 3, "vis_encode_type": "simple", "memory": null, "goal_conditioning_type": "hyper", "deterministic": false}, "reward_signals": {"extrinsic": {"gamma": 0.99, "strength": 1.0, "network_settings": {"normalize": false, "hidden_units": 128, "num_layers": 2, "vis_encode_type": "simple", "memory": null, "goal_conditioning_type": "hyper", "deterministic": false}}}, "init_path": null, "keep_checkpoints": 5, "even_checkpoints": false, "max_steps": 50000000, "time_horizon": 512, "summary_freq": 10000, "threaded": false, "self_play": {"save_steps": 50000, "team_change": 200000, "swap_steps": 2000, "window": 10, "play_against_latest_model_ratio": 0.7, "initial_elo": 1200.0}, "behavioral_cloning": null}}, "env_settings": {"env_path": "training-envs-executables/SoccerTwos/SoccerTwos.exe", "env_args": null, "base_port": 5005, "num_envs": 1, "num_areas": 1, "seed": -1, "max_lifetime_restarts": 10, "restarts_rate_limit_n": 1, "restarts_rate_limit_period_s": 60}, "engine_settings": {"width": 84, "height": 84, "quality_level": 5, "time_scale": 20, "target_frame_rate": -1, "capture_frame_rate": 60, "no_graphics": true}, "environment_parameters": null, "checkpoint_settings": {"run_id": "SoccerTwos", "initialize_from": null, "load_model": false, "resume": true, "force": false, "train_model": false, "inference": false, "results_dir": "results"}, "torch_settings": {"device": null}, "debug": false}
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+ default_settings: null
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+ behaviors:
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+ SoccerTwos:
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+ trainer_type: poca
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+ hyperparameters:
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+ batch_size: 512
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+ buffer_size: 20480
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+ learning_rate: 0.0003
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+ beta: 0.005
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+ normalize: false
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+ hidden_units: 512
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+ num_layers: 3
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+ vis_encode_type: simple
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+ memory: null
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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ gamma: 0.99
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+ strength: 1.0
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+ network_settings:
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+ normalize: false
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+ hidden_units: 128
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+ num_layers: 2
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+ vis_encode_type: simple
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+ memory: null
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+ summary_freq: 10000
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+ threaded: false
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+ self_play:
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+ save_steps: 50000
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+ team_change: 200000
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+ swap_steps: 2000
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+ window: 10
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+ initial_elo: 1200.0
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+ env_path: training-envs-executables/SoccerTwos/SoccerTwos.exe
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+ env_args: null
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+ base_port: 5005
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+ num_envs: 1
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+ num_areas: 1
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+ seed: -1
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+ max_lifetime_restarts: 10
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+ restarts_rate_limit_n: 1
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+ restarts_rate_limit_period_s: 60
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+ engine_settings:
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+ width: 84
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+ height: 84
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+ quality_level: 5
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+ time_scale: 20
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+ no_graphics: true
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+ environment_parameters: null
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+ checkpoint_settings:
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+ run_id: SoccerTwos
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+ initialize_from: null
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+ load_model: false
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+ resume: true
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+ force: false
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+ train_model: false
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+ inference: false
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+ results_dir: results
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+ torch_settings:
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+ device: null
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+ debug: false
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