Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use mayx95/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use mayx95/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="mayx95/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from mayx95/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/mayx95/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://mayx95/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/mayx95/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.64 kB
- Xet hash:
- 65101a49fd624d12b524172c69eb83f945815a499271e079705414c12c66d7ca
- Size of remote file:
- 2.64 kB
- SHA256:
- 9b8323724ac897b4f157787472449d76256fdd0b4c8644097edb923c26b95dfa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.