
  # **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)
```

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="epsil/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()

```

### Created by Saurabh Mishra

Made with 💖  in India
  
{"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": "124.30 +/- 74.63", "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)


### Created by Saurabh Mishra

Made with   in India
  
# 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) ### Created by Saurabh Mishra

Made with   in India
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) ### Created by Saurabh Mishra

Made with   in India
passage: 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)### Created by Saurabh Mishra

Made with   in India