Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use mjschock/a2c-AntBulletEnv-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use mjschock/a2c-AntBulletEnv-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="mjschock/a2c-AntBulletEnv-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from mjschock/a2c-AntBulletEnv-v0: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/mjschock/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://mjschock/a2c-AntBulletEnv-v0/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/mjschock/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
2.14 kB
- Xet hash:
- ba5d4cafeb889b4d73e7675fdd4de37d2123509555ced1f71e2fc921d1d71796
- Size of remote file:
- 2.14 kB
- SHA256:
- b91431073a1fbdc92b3dd78031829654b0390f2fcbbd66cf2df6b7c5d3cfa51f
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