Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use LaithSakk/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use LaithSakk/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="LaithSakk/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from LaithSakk/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.63 kB
-
https://huggingface.co/LaithSakk/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://LaithSakk/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/LaithSakk/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.63 kB
- Xet hash:
- 932b88c45c40ab259f34b4916cb054d048444b8d3d6bf45322599ee882b3ee4f
- Size of remote file:
- 2.63 kB
- SHA256:
- fd16df9ed27fe1916753eedfcbaa48c4d6b97f7eccc54752435aba03ce2764fd
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