Reinforcement Learning
stable-baselines3
LunarLander-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use AAAcharlie/ppo-LunarLander-v3-ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use AAAcharlie/ppo-LunarLander-v3-ppo with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="AAAcharlie/ppo-LunarLander-v3-ppo", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing LunarLander-v3
This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library.
Usage (with Stable-baselines3)
Install dependencies
pip install stable-baselines3 gymnasium box2d-py huggingface_sb3
```python
import gymnasium as gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
repo_id = "AAAchalie/ppo-LunarLander-v3-ppo"
filename = "ppo-LunarLander-v3-ppo.zip"
model = load_from_hub(repo_id, filename)
env = gym.make("LunarLander-v3", render_mode="human")
obs, _ = env.reset()
done = False
while not done:
action, _ = model.predict(obs)
obs, reward, terminated, truncated, _ = env.step(action)
done = terminated or truncated
...
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Evaluation results
- mean_reward on LunarLander-v3self-reported275.05 +/- 11.26