| tags: | |
| - ML-Agents-Pyramids | |
| - ppo | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - ml-agents | |
| library_name: ml-agents | |
| 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: -100 | |
| name: mean_reward | |
| verified: false | |
| # PPO Agent playing ML-Agents-Pyramids | |
| This is a trained PPO agent playing the ML-Agents-Pyramids environment | |
| using Unity ML-Agents. | |
| 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) | |
| ## Training | |
| The model was trained using Unity ML-Agents and exported to ONNX format. | |