| library_name: ml-agents | |
| tags: | |
| - SoccerTwos | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - ML-Agents-SoccerTwos | |
| # **poca** Agent playing **SoccerTwos** | |
| This is a trained model of a **poca** agent playing **SoccerTwos** | |
| using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). | |
| ## Usage (with ML-Agents) | |
| The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ | |
| We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: | |
| - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your | |
| browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction | |
| - A *longer tutorial* to understand how works ML-Agents: | |
| https://huggingface.co/learn/deep-rl-course/unit5/introduction | |
| ### Resume the training | |
| ```bash | |
| mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume | |
| ``` | |
| ### Watch your Agent play | |
| You can watch your agent **playing directly in your browser** | |
| 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity | |
| 2. Step 1: Find your model_id: vagi/poca-SoccerTwos-v2.1 | |
| 3. Step 2: Select your *.nn /*.onnx file | |
| 4. Click on Watch the agent play 👀 | |