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juansebashr
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ppo-SnowballTarget

Reinforcement Learning
ml-agents
TensorBoard
ONNX
unity-ml-agents
deep-reinforcement-learning
ML-Agents-SnowballTarget
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  1. README.md +31 -0
  2. SnowballTarget.onnx +3 -0
  3. SnowballTarget/SnowballTarget-149949.onnx +3 -0
  4. SnowballTarget/SnowballTarget-149949.pt +3 -0
  5. SnowballTarget/SnowballTarget-199973.onnx +3 -0
  6. SnowballTarget/SnowballTarget-199973.pt +3 -0
  7. SnowballTarget/SnowballTarget-200101.onnx +3 -0
  8. SnowballTarget/SnowballTarget-200101.pt +3 -0
  9. SnowballTarget/SnowballTarget-49936.onnx +3 -0
  10. SnowballTarget/SnowballTarget-49936.pt +3 -0
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  15. config.json +1 -0
  16. configuration.yaml +77 -0
  17. run_logs/Player-0.log +329 -0
  18. run_logs/timers.json +575 -0
  19. run_logs/training_status.json +65 -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-SnowballTarget
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+ library_name: ml-agents
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+ ---
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+
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+ # **ppo** Agent playing **SnowballTarget**
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+ This is a trained model of a **ppo** agent playing **SnowballTarget** 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-SnowballTarget
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+ 2. Step 1: Write your model_id: juansebashr/ppo-SnowballTarget
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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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+ SnowballTarget:
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