
  # **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, and huggingface_sb3)
  To use this model make sure you are running Python version 3.7.13. You can use [pyenv](https://github.com/pyenv/pyenv) to manage multiple versions of Python on your system.
  
  ### Install required packages:
  ```bash
pip install stable-baselines3
pip install huggingface_sb3
pip install pickle5
pip install Box2D
pip install pyglet
```

You can use this simple script as a base to evaluate and run the model: 
```python
import gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
from stable_baselines3.common.evaluation import evaluate_policy

# Download the model from the huggingface hub
checkpoint = load_from_hub(
    repo_id="kalmufti/PPO-LunarLander-v2",
    filename="ppo-LunarLander-v2.zip",
)
# Load the policy
model = PPO.load(checkpoint)
# Create an environment
env = gym.make("LunarLander-v2")
# Optional - evaluate the agent means
mean_reward, std_reward = evaluate_policy(
    model, env, render=False, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# Watch the agent playing the environment
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()
```
{"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": "275.34 +/- 14.56", "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, and huggingface_sb3)
  To use this model make sure you are running Python version 3.7.13. You can use pyenv to manage multiple versions of Python on your system.
  
  ### Install required packages:
  

You can use this simple script as a base to evaluate and run the model: 

# 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, and huggingface_sb3)
  To use this model make sure you are running Python version 3.7.13. You can use pyenv to manage multiple versions of Python on your system.
  
  ### Install required packages:
  

You can use this simple script as a base to evaluate and run the model:
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, and huggingface_sb3)
  To use this model make sure you are running Python version 3.7.13. You can use pyenv to manage multiple versions of Python on your system.
  
  ### Install required packages:
  

You can use this simple script as a base to evaluate and run the model:
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, and huggingface_sb3)
  To use this model make sure you are running Python version 3.7.13. You can use pyenv to manage multiple versions of Python on your system.
  
  ### Install required packages:
  

You can use this simple script as a base to evaluate and run the model: