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: 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>
      