Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use ThomasSimonini/ppo-LunarLander-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use ThomasSimonini/ppo-LunarLander-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="ThomasSimonini/ppo-LunarLander-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download LunarLander-v2.zip from ThomasSimonini/ppo-LunarLander-v2: direct link, hf CLI and curl.
- Browser
- Download file 144 kB
-
https://huggingface.co/ThomasSimonini/ppo-LunarLander-v2/resolve/main/LunarLander-v2.zip
- Command line
-
hf download hf://ThomasSimonini/ppo-LunarLander-v2/LunarLander-v2.zip
-
curl -L -o LunarLander-v2.zip https://huggingface.co/ThomasSimonini/ppo-LunarLander-v2/resolve/main/LunarLander-v2.zip
144 kB
- Xet hash:
- e5412274c0c1ad7b35fcba385682f9c787919e46d248cf38cdd910400fd54c59
- Size of remote file:
- 144 kB
- SHA256:
- 29203bb91c21c83b5b5c968131e86f44df1165f924377e43800b7e27bba447e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.