Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use OmBenz/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use OmBenz/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="OmBenz/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from OmBenz/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.66 kB
-
https://huggingface.co/OmBenz/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://OmBenz/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/OmBenz/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.66 kB
- Xet hash:
- f52a86eb4ac48cee65f3dc908a336abfef53e4d6e204d521acd7a8f14fd462e1
- Size of remote file:
- 2.66 kB
- SHA256:
- 8d118328f2ecda6efee93cedf3025c046cda77996abc36a18c13405ae3dc699f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.