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katk31
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poca-SoccerTwos

Reinforcement Learning
ml-agents
TensorBoard
ONNX
SoccerTwos
deep-reinforcement-learning
ML-Agents-SoccerTwos
Model card Files Metrics Community
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  1. README.md +35 -0
  2. SoccerTwos.onnx +3 -0
  3. SoccerTwos/SoccerTwos-3499969.onnx +3 -0
  4. SoccerTwos/SoccerTwos-3499969.pt +3 -0
  5. SoccerTwos/SoccerTwos-3999893.onnx +3 -0
  6. SoccerTwos/SoccerTwos-3999893.pt +3 -0
  7. SoccerTwos/SoccerTwos-4499976.onnx +3 -0
  8. SoccerTwos/SoccerTwos-4499976.pt +3 -0
  9. SoccerTwos/SoccerTwos-4999986.onnx +3 -0
  10. SoccerTwos/SoccerTwos-4999986.pt +3 -0
  11. SoccerTwos/SoccerTwos-5192526.onnx +3 -0
  12. SoccerTwos/SoccerTwos-5192526.pt +3 -0
  13. SoccerTwos/checkpoint.pt +3 -0
  14. SoccerTwos/events.out.tfevents.1717873604.83915742c4fc.1408.0 +3 -0
  15. config.json +1 -0
  16. configuration.yaml +84 -0
  17. run_logs/Player-0.log +660 -0
  18. run_logs/timers.json +424 -0
  19. run_logs/training_status.json +66 -0
README.md ADDED
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+ ---
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+ library_name: ml-agents
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+ tags:
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+ - SoccerTwos
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - ML-Agents-SoccerTwos
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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**
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+ 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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
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+
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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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+ - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
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+ browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
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+ - A *longer tutorial* to understand how works ML-Agents:
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+ https://huggingface.co/learn/deep-rl-course/unit5/introduction
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+
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+ ### Resume the training
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+ ```bash
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+ mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
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+ ```
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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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
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+ 2. Step 1: Find your model_id: katk31/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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config.json ADDED
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+ {"default_settings": null, "behaviors": {"SoccerTwos": {"trainer_type": "poca", "hyperparameters": {"batch_size": 2048, "buffer_size": 20480, "learning_rate": 0.0003, "beta": 0.005, "epsilon": 0.2, "lambd": 0.95, "num_epoch": 3, "learning_rate_schedule": "constant", "beta_schedule": "constant", "epsilon_schedule": "constant"}, "checkpoint_interval": 500000, "network_settings": {"normalize": false, "hidden_units": 512, "num_layers": 2, "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": 1000, "summary_freq": 10000, "threaded": false, "self_play": {"save_steps": 50000, "team_change": 200000, "swap_steps": 2000, "window": 10, "play_against_latest_model_ratio": 0.5, "initial_elo": 1200.0}, "behavioral_cloning": null}}, "env_settings": {"env_path": "./training-envs-executables/SoccerTwos/SoccerTwos.x86_64", "env_args": null, "base_port": 5005, "num_envs": 1, "num_areas": 1, "timeout_wait": 60, "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, "no_graphics_monitor": false}, "environment_parameters": null, "checkpoint_settings": {"run_id": "SoccerTwos", "initialize_from": null, "load_model": false, "resume": false, "force": true, "train_model": false, "inference": false, "results_dir": "results"}, "torch_settings": {"device": null}, "debug": false}
configuration.yaml ADDED
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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: 2048
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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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+ epsilon: 0.2
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+ lambd: 0.95
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+ num_epoch: 3
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+ learning_rate_schedule: constant
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+ beta_schedule: constant
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+ epsilon_schedule: constant
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+ checkpoint_interval: 500000
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+ network_settings:
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+ normalize: false
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+ hidden_units: 512
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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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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ reward_signals:
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+ extrinsic:
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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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+ goal_conditioning_type: hyper
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+ deterministic: false
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+ init_path: null
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+ keep_checkpoints: 5
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+ even_checkpoints: false
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+ max_steps: 50000000
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+ time_horizon: 1000
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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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+ play_against_latest_model_ratio: 0.5
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+ initial_elo: 1200.0
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+ behavioral_cloning: null
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+ env_settings:
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+ env_path: ./training-envs-executables/SoccerTwos/SoccerTwos.x86_64
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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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+ timeout_wait: 60
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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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+ target_frame_rate: -1
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+ capture_frame_rate: 60
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+ no_graphics: true
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+ no_graphics_monitor: false
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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: false
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+ force: true
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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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+ Mono path[0] = '/content/ml-agents/training-envs-executables/SoccerTwos/SoccerTwos_Data/Managed'
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