Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use pinaggle/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use pinaggle/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="pinaggle/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from pinaggle/a2c-PandaReachDense-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.38 kB
-
https://huggingface.co/pinaggle/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://pinaggle/a2c-PandaReachDense-v2/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/pinaggle/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
2.38 kB
- Xet hash:
- 1b912eb1c219a5c1453e9ea33242266ea3380063257f14617956dbdf8034e74b
- Size of remote file:
- 2.38 kB
- SHA256:
- 42ade98297090fcc4d20ad0dfbe7366ef39e44641f3a07f15754a57bf0030783
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.