| library_name: stable-baselines3 | |
| tags: | |
| - LunarLander-v2 | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| model-index: | |
| - name: PPO MLP Policy Architecture | |
| results: | |
| - metrics: | |
| - type: mean_reward | |
| value: 143.60 +/- 115.75 | |
| name: mean_reward | |
| task: | |
| type: reinforcement-learning | |
| name: reinforcement-learning | |
| dataset: | |
| name: LunarLander-v2 | |
| type: LunarLander-v2 | |
| # **PPO MLP Policy Architecture** Agent playing **LunarLander-v2** | |
| This is a trained model of a **PPO MLP Policy Architecture** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). | |
| ## Usage (with Stable-baselines3) | |
| TODO: Add your code | |