readme: ---
  library_name: stable-baselines3
  tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: PPO
    results:
    - task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: LunarLander-v2
        type: LunarLander-v2
      metrics:
      - type: mean_reward
        value: 275.80 +/- 20.96
        name: mean_reward
        verified: false
  ---
  
  # **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)
  
  Usage (with Stable-baselines3)
  Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
  
  ```
  pip install stable-baselines3
  pip install huggingface_sb3
  ```
  Then, you can use the model like this:
  
  ```python
  import gym
  
  from huggingface_sb3 import load_from_hub
  from stable_baselines3 import PPO
  from stable_baselines3.common.evaluation import evaluate_policy
  
  checkpoint = load_from_hub(repo_id="Felipe474/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
  model = PPO.load(checkpoint)
  
  # Evaluate the agent
  eval_env = gym.make('LunarLander-v2')
  mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
  print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
  
  # Watch the agent play
  obs = eval_env.reset()
  for i in range(1000):
      action, _state = model.predict(obs)
      obs, reward, done, info = eval_env.step(action)
      eval_env.render()
      if done:
          obs = eval_env.reset()
  eval_env.close()
  