Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use msladic/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use msladic/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="msladic/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from msladic/a2c-PandaReachDense-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.68 kB
-
https://huggingface.co/msladic/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://msladic/a2c-PandaReachDense-v2/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/msladic/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
2.68 kB
- Xet hash:
- 7c0c457c144b47536701c46a682b6fef806388d551953b91da68a08359b56237
- Size of remote file:
- 2.68 kB
- SHA256:
- 819dc1018eef8a14706ba17cc2b71d15bdb245ded625986934258a0df0e55808
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.