| --- |
| library_name: ml-agents |
| tags: |
| - deep-reinforcement-learning |
| - reinforcement-learning |
| - PyTorch |
| --- |
| |
| # **poca** Agents: **CubeAgent** and **RedAgent** |
| This is two trained models of a **poca** configuration: **CubeAgent** and **RedAgent** |
| 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. [Oranges Vs Reds Env](https://huggingface.co/spaces/SamuelM0422/OrangesVsReds) |
| 2. [Reds Training Env](https://huggingface.co/spaces/SamuelM0422/HunterEnv) |
| 3. [Oranges Training Env](https://huggingface.co/spaces/SamuelM0422/CubeAgentEnv) |
| |
