readme: #@title
  ---
  library_name: stable-baselines3
  tags:
  - LunarLander-v2
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: PPO
    results:
    - metrics:
      - type: mean_reward
        value: 290.76 +/- 18.71
        name: mean_reward
      task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: LunarLander-v2
        type: LunarLander-v2
  ---
  # {name_of_your_repo}
  
  This is a pre-trained model of a {algo} agent playing {environment} using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
  
  ### 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
  
  # Retrieve the model from the hub
  ## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
  ## filename = name of the model zip file from the repository
  checkpoint = load_from_hub(repo_id="{repo_id}", filename="{filename}.zip")
  model = PPO.load(checkpoint)
  
  # Evaluate the agent
  eval_env = gym.make('{environment}')
  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 = env.reset()
  for i in range(1000):
      action, _state = model.predict(obs)
      obs, reward, done, info = env.step(action)
      env.render()
      if done:
          obs = env.reset()
  env.close()
  ```
  
  ### Evaluation Results
  Mean_reward: {your_evaluation_results}
  
  ### Demo
  <video src="https://huggingface.co/ncduy/ppo-LunarLander-v2/resolve/main/output.mp4" controls autoplay loop></video>