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: 282.19 +/- 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)
  ```