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: 295.51 +/- 20.94
        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
  import gym
  
  from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub
  from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
  
  from stable_baselines3 import PPO
  from stable_baselines3.common.evaluation import evaluate_policy
  from stable_baselines3.common.env_util import make_vec_env
  
  # Create the environment
  env = make_vec_env('LunarLander-v2', n_envs=16)
  
  model = PPO(
      policy = 'MlpPolicy',
      env = env,
      n_steps = 1024,
      batch_size = 64,
      n_epochs = 4,
      gamma = 0.999,
      gae_lambda = 0.98,
      ent_coef = 0.01,
      learning_rate=0.0004,
      verbose=1)
  
  # Train it for 3,000,000 timesteps
  model.learn(total_timesteps=3000000)
  # Save the model
  model_name = "ppo-LunarLander-v2"
  model.save(model_name)
  
  ...
  ```
  