Reinforcement Learning
stable-baselines3
BipedalWalkerHardcore-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use sb3/ppo-BipedalWalkerHardcore-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use sb3/ppo-BipedalWalkerHardcore-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="sb3/ppo-BipedalWalkerHardcore-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from sb3/ppo-BipedalWalkerHardcore-v3: direct link, hf CLI and curl.
- Browser
- Download file 6.5 kB
-
https://huggingface.co/sb3/ppo-BipedalWalkerHardcore-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://sb3/ppo-BipedalWalkerHardcore-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/sb3/ppo-BipedalWalkerHardcore-v3/resolve/main/vec_normalize.pkl
6.5 kB
- Xet hash:
- 639c31806a1f62f0821c3498662c5074bea8bd04ad5a1b8883f36d0e888e0734
- Size of remote file:
- 6.5 kB
- SHA256:
- b440be467bf33c0a474ea1cd4c5ab461a167e777b5bee2f55555e28d432baa8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.