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: 243.69 +/- 18.42
        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)
  
  ```python
  from huggingface_sb3 import load_from_hub
  from stable_baselines3 import PPO
  from stable_baselines3.common.env_util import make_vec_env
  from stable_baselines3.common.evaluation import evaluate_policy
  
  # Download checkpoint
  model_file = load_from_hub("umersheikh846/ppo-LunarLander-v2", "ppo-LunarLander-v2.zip")
  # Load the model
  model = PPO.load(model_file)
  
  env = make_vec_env("LunarLander-v2", n_envs=1) # for training, 16 n_envs have been used, while 1 enough
  
  # Evaluate the trained model with evaluate policy from SB3
  print("Evaluating model")
  mean_reward, std_reward = evaluate_policy(model,env, n_eval_episodes=10, deterministic=True)
  print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
  
  # Start a new episode
  obs = env.reset()
  
  while True:
        action, _states = model.predict(obs, deterministic=True)
        obs, rewards, dones, info = env.step(action)
        env.render()
  
  
  ...
  ```
  