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eldraco
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poca-SoccerTwos

Reinforcement Learning
ml-agents
TensorBoard
ONNX
unity-ml-agents
deep-reinforcement-learning
ML-Agents-SoccerTwos
Model card Files Metrics Community
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  1. README.md +31 -0
  2. SoccerTwos/SoccerTwos-1499964.onnx +3 -0
  3. SoccerTwos/SoccerTwos-1499964.pt +3 -0
  4. SoccerTwos/SoccerTwos-1999968.onnx +3 -0
  5. SoccerTwos/SoccerTwos-1999968.pt +3 -0
  6. SoccerTwos/SoccerTwos-2499956.onnx +3 -0
  7. SoccerTwos/SoccerTwos-2499956.pt +3 -0
  8. SoccerTwos/SoccerTwos-499618.onnx +3 -0
  9. SoccerTwos/SoccerTwos-499618.pt +3 -0
  10. SoccerTwos/SoccerTwos-999991.onnx +3 -0
  11. SoccerTwos/SoccerTwos-999991.pt +3 -0
  12. SoccerTwos/checkpoint.pt +3 -0
  13. SoccerTwos/events.out.tfevents.1675601257.groot.559152.0 +3 -0
  14. config.json +1 -0
  15. configuration.yaml +46 -0
  16. run_logs/Player-0.log +356 -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: eldraco/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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+ {"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}}}, "keep_checkpoints": 5, "max_steps": 10500000, "time_horizon": 1000, "summary_freq": 10000, "self_play": {"save_steps": 50000, "team_change": 200000, "swap_steps": 2000, "window": 10, "play_against_latest_model_ratio": 0.5, "initial_elo": 1200.0}}}}
configuration.yaml ADDED
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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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+ keep_checkpoints: 5
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+ max_steps: 10500000
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+ time_horizon: 1000
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+ summary_freq: 10000
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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
run_logs/Player-0.log ADDED
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+ Mono path[0] = '/home/sebas/research/hugging-face-course/soccer/ml-agents/training-envs-executables/SoccerTwos_Data/Managed'
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+ Mono config path = '/home/sebas/research/hugging-face-course/soccer/ml-agents/training-envs-executables/SoccerTwos_Data/MonoBleedingEdge/etc'
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+ [Subsystems] Discovering subsystems at path /home/sebas/research/hugging-face-course/soccer/ml-agents/training-envs-executables/SoccerTwos_Data/UnitySubsystems
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+ Registered Communicator in Agent.
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