Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Emperor/a2c-AntBulletEnv-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Emperor/a2c-AntBulletEnv-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Emperor/a2c-AntBulletEnv-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from Emperor/a2c-AntBulletEnv-v0: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/Emperor/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://Emperor/a2c-AntBulletEnv-v0/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/Emperor/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
2.14 kB
- Xet hash:
- 4f70170c997d557e44fb241c11efb21c4ac8b3eacc83d77c5afc507885be97e6
- Size of remote file:
- 2.14 kB
- SHA256:
- 980ac7fba9ddf013dcae7f2fd00088d670510975e1ec11af3dc9fdb0e3a4bae2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.