Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Arindam75/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Arindam75/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Arindam75/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from Arindam75/a2c-PandaReachDense-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/Arindam75/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://Arindam75/a2c-PandaReachDense-v2/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/Arindam75/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
2.39 kB
- Xet hash:
- ef41e84bcc0b6afe981b1447c09a7d10bd4e54bfdbc239d7a65b33ad1a7f5673
- Size of remote file:
- 2.39 kB
- SHA256:
- 68a7ad2d33ab863a8eb53cbfb2e56fbf4c6ec29bc2f3479a97a6fc56a6dccbfd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.