
  # **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)
  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="vbertret/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
  model = PPO.load(checkpoint)
  
  # Evaluate the agent
  eval_env = gym.make('LunarLander-v2')
  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 = eval_env.reset()
  for i in range(1000):
      action, _state = model.predict(obs)
      obs, reward, done, info = eval_env.step(action)
      eval_env.render()
      if done:
          obs = eval_env.reset()
  eval_env.close()
  ```
  
{"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": "287.79 +/- 16.83", "name": "mean_reward"}]}]}]}
stable-baselines3 LunarLander-v2 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


  # PPO Agent playing LunarLander-v2
  This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
  
  ## Usage (with Stable-baselines3)
  Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
  
  Then, you can use the model like this:
  
  
# PPO Agent playing LunarLander-v2
  This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
  
  ## Usage (with Stable-baselines3)
  Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
  
  Then, you can use the model like this:
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # PPO Agent playing LunarLander-v2
  This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
  
  ## Usage (with Stable-baselines3)
  Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
  
  Then, you can use the model like this:
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# PPO Agent playing LunarLander-v2
  This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
  
  ## Usage (with Stable-baselines3)
  Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
  
  Then, you can use the model like this: