| tags: | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - ml-agents | |
| - unity-ml-agents | |
| - poca | |
| - SoccerTwos | |
| - deep-rl-course | |
| - ML-Agents-SoccerTwos | |
| library_name: ml-agents | |
| # ML-Agents SoccerTwos Model for Deep RL Course Unit 7 | |
| This is a trained multi-agent reinforcement learning model using the **POCA (MA-POCA)** trainer algorithm to play soccer in the Unity `SoccerTwos` environment. | |
| This model was trained as part of the [Hugging Face Deep Reinforcement Learning Course](https://huggingface.co). | |
| ## Environment Details | |
| * **Name**: SoccerTwos | |
| * **Number of Teams**: 2 (Blue vs Purple) | |
| * **Agents per Team**: 2 | |
| * **Goal**: Outscore the opposing team within the time limit. | |
| ## Training Configuration | |
| The training was conducted with shortened parameters to optimize computation overhead: | |
| * **Max Steps**: 500,000 | |
| * **Hidden Units**: 128 | |
| * **Batch Size**: 1024 | |
| * **Trainer Type**: poca | |
| ## How to Use | |
| To inspect or utilize this policy file locally within your ML-Agents environment workspace, look at the uploaded `.onnx` binary file inside this repository. | |