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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-48499956.onnx +3 -0
  4. SoccerTwos/SoccerTwos-48499956.pt +3 -0
  5. SoccerTwos/SoccerTwos-48999924.onnx +3 -0
  6. SoccerTwos/SoccerTwos-48999924.pt +3 -0
  7. SoccerTwos/SoccerTwos-49499992.onnx +3 -0
  8. SoccerTwos/SoccerTwos-49499992.pt +3 -0
  9. SoccerTwos/SoccerTwos-49999898.onnx +3 -0
  10. SoccerTwos/SoccerTwos-49999898.pt +3 -0
  11. SoccerTwos/SoccerTwos-50000150.onnx +3 -0
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  15. config.json +1 -0
  16. configuration.yaml +82 -0
  17. run_logs/Player-0.log +0 -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: BanUrsus/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
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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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+ vis_encode_type: simple
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+ memory: null
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+ deterministic: false
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+ strength: 1.0
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+ num_layers: 2
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+ vis_encode_type: simple
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+ deterministic: false
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+ even_checkpoints: false
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+ time_horizon: 1000
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+ self_play:
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+ window: 10
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+ width: 84
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