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: 253.91 +/- 68.63
        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 = 8,
              gamma = 0.995,
              gae_lambda = 1,
              ent_coef = 0.001,
              verbose=1
          )
  
  model.learn(total_timesteps=2_000_000, log_interval=25, progress_bar=True)
  
  model_name = "ppo-LunarLander-v2"
  
  # Evaluate the agent
  # Create a new environment for evaluation
  eval_env = gym.make("LunarLander-v2")
  
  # Evaluate the model with 10 evaluation episodes and deterministic=True
  mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
  
  # Print the results
  print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
  
  # Upload to Hugging Face Hub
  ...
  ```
  