| --- |
| license: mit |
| tags: |
| - reinforcement-learning |
| - ppo |
| - gymnasium |
| - deep-rl-course |
| - LunarLander-v2 |
| model-index: |
| - name: PPO LunarLander-v3 |
| results: |
| - task: |
| type: reinforcement-learning |
| name: Reinforcement Learning |
| dataset: |
| name: LunarLander-v3 |
| type: LunarLander-v3 |
| metrics: |
| - type: mean_reward |
| value: -229.885 |
| name: Mean Reward |
| --- |
| # PPO from scratch — LunarLander-v3 |
|
|
| Trained using a CleanRL-style PyTorch PPO implementation adapted for Gymnasium. |
|
|
| Evaluation over 10 episodes: **-229.89 ± 22.10**. |
|
|
| - `model.pt`: actor-critic PyTorch state dict |
| - `ppo.py`: full training/evaluation/upload source |
| - `replay.mp4`: evaluation video (if rendering succeeded) |
| - `hyperparameters.json`: training settings |
| - `eval_results.json`: evaluation results |
|
|
| To run locally, install PyTorch, `gymnasium[box2d]`, `imageio`, `imageio-ffmpeg`, `tensorboard`, and `huggingface_hub`. |
|
|
