Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use pratsy/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use pratsy/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="pratsy/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from pratsy/a2c-PandaReachDense-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/pratsy/a2c-PandaReachDense-v2/resolve/97dea07a36461ba4abd99107180f20050ee3b2d5/vec_normalize.pkl
- Command line
-
hf download hf://pratsy/a2c-PandaReachDense-v2@97dea07a36461ba4abd99107180f20050ee3b2d5/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/pratsy/a2c-PandaReachDense-v2/resolve/97dea07a36461ba4abd99107180f20050ee3b2d5/vec_normalize.pkl
2.39 kB
- Xet hash:
- 27bb2f4fa9905ad50419f57819456fb2e3bfca5174fb83b4dbf7ee9a7cef3025
- Size of remote file:
- 2.39 kB
- SHA256:
- 29b0895ed47749615970f7c55de6d88b895fb3368a00fe48988d24e808f923e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.