readme: ---
  library_name: stable-baselines3
  tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: PPO
    results:
    - metrics:
      - type: mean_reward
        value: 286.34 +/- 10.43
        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)
    ```model = PPO(
      policy = 'MlpPolicy',
      env = env,
      n_steps = 1024,
      batch_size = 32,
      n_epochs = 4,
      gamma = 0.9990,
      gae_lambda = 0.995,
      ent_coef = 0.005,
      verbose=1)
     
     model.learn(total_timesteps=2000000)```
    