Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use cgwell/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use cgwell/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="cgwell/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from cgwell/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.65 kB
-
https://huggingface.co/cgwell/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://cgwell/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/cgwell/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.65 kB
- Xet hash:
- 29a8d581e0c05e325b4c355caf047380479512ca5648a0cc5876dc2056e7a64c
- Size of remote file:
- 2.65 kB
- SHA256:
- 53d094e091898e83db38c7e6a1ea41c0603f526e80cbe7dee4c5f6992b0498f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.