Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use hungtrab/LunarLander-v2-ppo-MlpPolicy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use hungtrab/LunarLander-v2-ppo-MlpPolicy with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="hungtrab/LunarLander-v2-ppo-MlpPolicy", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.
See the python and dependencies version in ppo-LunarLander-v2/system_info.txt.
Installation
The requirements in the original notebook doesn't work now (August 2025), use the following instead
pip install stable-baselines3==2.0.0a5
pip install swig
pip install gymnasium
pip install box2d-py
pip install huggingface_sb3
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
repo_id = "hungtrab/LunarLander-v2-ppo-MlpPolicy" # The repo_id
filename = "ppo-LunarLander-v2.zip" # The model filename.zip
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, print_system_info=True)
...
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Evaluation results
- mean_reward on LunarLander-v2self-reported262.24 +/- 13.18