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--- |
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library_name: ml-agents |
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tags: |
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- deep-reinforcement-learning |
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- reinforcement-learning |
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- PyTorch |
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--- |
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# **poca** Agents: **CubeAgent** and **RedAgent** |
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This is two trained models of a **poca** configuration: **CubeAgent** and **RedAgent** |
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using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). |
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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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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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### 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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### Watch your Agent play |
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You can watch your agent **playing directly in your browser** |
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1. [Oranges Vs Reds Env](https://huggingface.co/spaces/SamuelM0422/OrangesVsReds) |
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2. [Reds Training Env](https://huggingface.co/spaces/SamuelM0422/HunterEnv) |
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3. [Oranges Training Env](https://huggingface.co/spaces/SamuelM0422/CubeAgentEnv) |
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