Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Emperor-WS/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Emperor-WS/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Emperor-WS/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from Emperor-WS/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.65 kB
-
https://huggingface.co/Emperor-WS/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://Emperor-WS/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/Emperor-WS/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.65 kB
- Xet hash:
- 25b317501398284695785d32ba2f887d40ac2150d2abb450c1df8d5c0d6e637d
- Size of remote file:
- 2.65 kB
- SHA256:
- d9e861265d15d6dd46e8734bca6bd93c75bf449d4ca4507c54f7b7d8a7d1cad3
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