Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use gmojko/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use gmojko/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="gmojko/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from gmojko/a2c-PandaReachDense-v2: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/gmojko/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://gmojko/a2c-PandaReachDense-v2/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/gmojko/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
3.06 kB
- Xet hash:
- 8be334d458f2e2c5ce37a306dbdf703b524d6cd08da5940f749a90686873a8a9
- Size of remote file:
- 3.06 kB
- SHA256:
- 8bc9139649d9b872943e902d8e64272cf5673f1aa69b3c19904260c89cea3b8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.