| library_name: ml-agents | |
| tags: | |
| - Pyramids | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - ML-Agents-Pyramids | |
| model-index: | |
| - name: PPO | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: ML-Agents-Pyramids | |
| type: ML-Agents-Pyramids | |
| metrics: | |
| - type: mean_reward | |
| value: 0 +/- 0 | |
| name: mean_reward | |
| # PPO Agent playing Pyramids | |
| This is a trained model of a PPO agent playing Pyramids | |
| using the Unity ML-Agents Library. | |
| This model was trained as part of the Hugging Face | |
| Deep Reinforcement Learning Course, Unit 5. | |
| ## Environment | |
| ML-Agents-Pyramids | |
| ## Algorithm | |
| Proximal Policy Optimization (PPO) | |
| ## Library | |
| Unity ML-Agents | |
| ## Model | |
| The trained agent is provided as an ONNX model. | |