Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use MUTSC/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use MUTSC/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="MUTSC/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e252c7ba2c75d6f5431eebb79bae7482b8c285a5de91c2f071437dffb9cf693a
- Size of remote file:
- 147 kB
- SHA256:
- 7b44368fd7cc79cb6475e203dfa37398a9eb68a1e633892d06d290ddfba0cd16
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.