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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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# **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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## Codes |
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Github repos(Give a star if found useful): |
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* https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots |
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* https://github.com/hishamcse/DRL-Renegades-Game-Bots |
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Kaggle Notebook: |
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* https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-7-marl-soccer-2vs2 |
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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. 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: hishamcse/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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