Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use sal-ops/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use sal-ops/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="sal-ops/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from sal-ops/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/sal-ops/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://sal-ops/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/sal-ops/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.64 kB
- Xet hash:
- e83520e6405a3df806ffd9031c588b83600a34fc93216edf79d02040d7040ea5
- Size of remote file:
- 2.64 kB
- SHA256:
- f82cf71f8eba4a88d037513e5a7839b4cdb3a9412095bdf90d0ffc49d8f23464
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.