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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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- ## 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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- ### 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: Esteban00007/poca-SoccerTwos
 
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  ---
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  # **poca** Agent playing **SoccerTwos**
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+ I am Esteban and this is my 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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  ### 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 my Agent play
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+ You can watch my 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: Esteban00007/poca-SoccerTwos