Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use undrwolf/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use undrwolf/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="undrwolf/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from undrwolf/a2c-PandaReachDense-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/undrwolf/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://undrwolf/a2c-PandaReachDense-v2/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/undrwolf/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
2.39 kB
- Xet hash:
- 971156cb4913724531ba8e0e29d52a1c2554e914d1523619a5671a384055f2f9
- Size of remote file:
- 2.39 kB
- SHA256:
- 31dc0d6eada5035dd8ee8fc7e3454609b38fcd21635ec8e328cc2bf29db39c3e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.