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: 282.88 +/- 14.89
        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)
  ```
    from typing import Callable
  
    def linear_schedule(initial_value: float) -> Callable[[float], float]:
        def func(progress_remaining: float) -> float:
            return progress_remaining * initial_value
    
        return func
    
    model = PPO(policy="MlpPolicy", env=env, verbose=1, n_epochs=10, learning_rate=linear_schedule(0.005), n_steps=1500)
  ```