| library_name: stable-baselines3 | |
| tags: | |
| - BipedalWalker-v3 | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| model-index: | |
| - name: Proximal Policy Optimisation (PPO) | |
| results: | |
| - task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: BipedalWalker-v3 | |
| type: BipedalWalker-v3 | |
| metrics: | |
| - type: mean_reward | |
| value: 209.63 +/- 82.30 | |
| name: mean_reward | |
| verified: false | |
| # **Proximal Policy Optimisation (PPO)** Agent playing **BipedalWalker-v3** | |
| This is a trained model of a **Proximal Policy Optimisation (PPO)** agent playing **BipedalWalker-v3** | |
| using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). | |
| ## Usage (with Stable-baselines3) | |
| TODO: Add your code | |
| ```python | |
| from stable_baselines3 import ... | |
| from huggingface_sb3 import load_from_hub | |
| ... | |
| ``` | |