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: 283.49 +/- 13.74
        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
  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
  checkpoint = load_from_hub("araffin/ppo-LunarLander-v2", "ppo-LunarLander-v2.zip")
  # Load the model
  model = PPO.load(checkpoint)
  
  env = make_vec_env("LunarLander-v2", n_envs=1)
  
  # Evaluate
  print("Evaluating model")
  mean_reward, std_reward = evaluate_policy(
      model,
      env,
      n_eval_episodes=20,
      deterministic=True,
  )
  print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
  
  # Start a new episode
  obs = env.reset()
  
  try:
      while True:
          action, _states = model.predict(obs, deterministic=True)
          obs, rewards, dones, info = env.step(action)
          env.render()
  except KeyboardInterrupt:
      pass
  ```
  
  ## Training code (with SB3)
  
  ```python
  from stable_baselines3 import PPO
  from stable_baselines3.common.env_util import make_vec_env
  from stable_baselines3.common.callbacks import EvalCallback
  
  # Create the environment
  env_id = "LunarLander-v2"
  n_envs = 16
  env = make_vec_env(env_id, n_envs=n_envs)
  
  # Create the evaluation envs
  eval_envs = make_vec_env(env_id, n_envs=5)
  
  # Adjust evaluation interval depending on the number of envs
  eval_freq = int(1e5)
  eval_freq = max(eval_freq // n_envs, 1)
  
  # Create evaluation callback to save best model
  # and monitor agent performance
  eval_callback = EvalCallback(
      eval_envs,
      best_model_save_path="./logs/",
      eval_freq=eval_freq,
      n_eval_episodes=10,
  )
  
  # Instantiate the agent
  # Hyperparameters from https://github.com/DLR-RM/rl-baselines3-zoo
  model = PPO(
      "MlpPolicy",
      env,
      n_steps=1024,
      batch_size=64,
      gae_lambda=0.98,
      gamma=0.999,
      n_epochs=4,
      ent_coef=0.01,
      verbose=1,
  )
  
  # Train the agent (you can kill it before using ctrl+c)
  try:
      model.learn(total_timesteps=int(5e6), callback=eval_callback)
  except KeyboardInterrupt:
      pass
  
  # Load best model
  model = PPO.load("logs/best_model.zip")
  ```
    