| library_name: stable-baselines3 | |
| tags: | |
| - LunarLander-v2 | |
| - deep-reinforcement-learning | |
| - reinforcement-learning | |
| - stable-baselines3 | |
| model-index: | |
| - name: PPO | |
| results: | |
| - metrics: | |
| - type: mean_reward | |
| value: 282.19 +/- 21.40 | |
| 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) | |
| ``` | |
| env = make_vec_env('LunarLander-v2', n_envs=24) | |
| model = PPO( | |
| policy = 'MlpPolicy', | |
| env = env, | |
| n_steps = 1024, | |
| batch_size = 64, | |
| n_epochs = 16, | |
| gamma = 0.999, | |
| gae_lambda = 0.98, | |
| ent_coef = 0.01, | |
| verbose=1) | |
| model.learn(total_timesteps=500000) | |
| ``` |