Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Manaro/a2c-AntBulletEnv-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Manaro/a2c-AntBulletEnv-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Manaro/a2c-AntBulletEnv-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from Manaro/a2c-AntBulletEnv-v0: direct link, hf CLI and curl.
- Browser
- Download file 2.18 kB
-
https://huggingface.co/Manaro/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://Manaro/a2c-AntBulletEnv-v0/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/Manaro/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
2.18 kB
- Xet hash:
- 0120f96a1fac878f6bbb765eda02a98259d381e64179c26c219d4f528600478a
- Size of remote file:
- 2.18 kB
- SHA256:
- 4ec3a83fc45e2972bdd0df2c3e529cdb35f665f25e43ff931f51efce2657d4aa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.