Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use FranEnguix/a2c-AntBulletEnv-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use FranEnguix/a2c-AntBulletEnv-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="FranEnguix/a2c-AntBulletEnv-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from FranEnguix/a2c-AntBulletEnv-v0: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/FranEnguix/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://FranEnguix/a2c-AntBulletEnv-v0/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/FranEnguix/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
2.14 kB
- Xet hash:
- 8a88fafdeb2a506edaa2d11e525cacc7ed81aa1e66a83d0f5577a1c204216484
- Size of remote file:
- 2.14 kB
- SHA256:
- 0579b2b3a5c90c618f60fa6ba695d49f7a3e4bdb0af536d0f9a9288775e926e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.