Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use QuickSilver007/a2c-AntBulletEnv-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use QuickSilver007/a2c-AntBulletEnv-v0 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="QuickSilver007/a2c-AntBulletEnv-v0", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from QuickSilver007/a2c-AntBulletEnv-v0: direct link, hf CLI and curl.
- Browser
- Download file 2.76 kB
-
https://huggingface.co/QuickSilver007/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://QuickSilver007/a2c-AntBulletEnv-v0/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/QuickSilver007/a2c-AntBulletEnv-v0/resolve/main/vec_normalize.pkl
2.76 kB
- Xet hash:
- dbc4ef47b815dc6aeead09dea5095507b849f4760bfd93e28c800842c1778e60
- Size of remote file:
- 2.76 kB
- SHA256:
- e17d65aa10666de9a22d9dd7424a3ff534bd1af35a19cf4d3c01d33ac7f315ad
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