Reinforcement Learning
stable-baselines3
PandaReachDense-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use LuisMBA/a2c-PandaReachDense-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use LuisMBA/a2c-PandaReachDense-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="LuisMBA/a2c-PandaReachDense-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from LuisMBA/a2c-PandaReachDense-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/LuisMBA/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://LuisMBA/a2c-PandaReachDense-v3/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/LuisMBA/a2c-PandaReachDense-v3/resolve/main/vec_normalize.pkl
2.64 kB
- Xet hash:
- 72ea3e78d3dd993eed1dd97892574ae26042f83e3538f288c4f4218000cc213c
- Size of remote file:
- 2.64 kB
- SHA256:
- 2659e95bcb073d433d5772221e6c8b0c22ebed6e9b8b2d9036c3fa2033205d29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.