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: 245.74 +/- 18.06
        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)
  
  ```python
  import gym
  
  from huggingface_sb3 import load_from_hub
  
  from stable_baselines3 import PPO
  from stable_baselines3.common.evaluation import evaluate_policy
  from stable_baselines3.common.env_util import make_vec_env
  
  env = make_vec_env('LunarLander-v2', n_envs=16)
  model = PPO('MlpPolicy', env, verbose=1)
  
  model.learn(total_timesteps=5 * 10**5)
  
  eval_env = gym.make('LunarLander-v2')
  mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=10, deterministic=True)
  print(f"Reward mean: {mean_reward:.2f}, Reward STD: {std_reward:.2f}")
  ```
  