Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use MakiPan/a2c-AntBulletEnv-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use MakiPan/a2c-AntBulletEnv-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="MakiPan/a2c-AntBulletEnv-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from MakiPan/a2c-AntBulletEnv-v0: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/MakiPan/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://MakiPan/a2c-AntBulletEnv-v0/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/MakiPan/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
2.14 kB
- Xet hash:
- 9655637ff527452ae7571e2aef79a35316ce995b44317dc6f661298ab7179b6d
- Size of remote file:
- 2.14 kB
- SHA256:
- 4ec4bffb9e76d4618697de67b6969af5265c10e709d35edddf3b0878b9f88686
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