Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Rebixa/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Rebixa/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Rebixa/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from Rebixa/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/Rebixa/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://Rebixa/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/Rebixa/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.64 kB
- Xet hash:
- da0aabbd85a88de567340173b47018d19ebb199276afd2380c7345c5d004a6fa
- Size of remote file:
- 2.64 kB
- SHA256:
- ee2f449da34b6a1313ed54e0875a6411e862c49a87b466407fd1b788aa0d7118
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.