| --- |
| library_name: stable-baselines3 |
| tags: |
| - LunarLander-v2 |
| - deep-reinforcement-learning |
| - reinforcement-learning |
| - stable-baselines3 |
| model-index: |
| - name: PPO |
| results: |
| - metrics: |
| - type: mean_reward |
| value: 282.71 +/- 20.92 |
| name: mean_reward |
| task: |
| type: reinforcement-learning |
| name: reinforcement-learning |
| dataset: |
| name: LunarLander-v2 |
| type: LunarLander-v2 |
| --- |
| |
| # **PPO** Agent playing **LunarLander-v2** |
| This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). |
| |
| ## Usage (with Stable-baselines3) |
| ## Parameters |
| |
| model = PPO( <br> |
| policy = "MlpPolicy", <br> |
| env = env, <br> |
| learning_rate = 0.0001, <br> |
| n_steps = 1024, <br> |
| batch_size = 32, <br> |
| n_epochs = 16, <br> |
| gamma = 0.999, <br> |
| ent_coef = 0.01, <br> |
| verbose = 1 <br> |
| ) <br> |
| |
