Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use kebei/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use kebei/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="kebei/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Download vec_normalize.pkl from kebei/a2c-PandaReachDense-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/kebei/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
- Command line
-
hf download hf://kebei/a2c-PandaReachDense-v2/vec_normalize.pkl
-
curl -L -o vec_normalize.pkl https://huggingface.co/kebei/a2c-PandaReachDense-v2/resolve/main/vec_normalize.pkl
2.39 kB
- Xet hash:
- ccef00a8081c07d75449d695463e96ca9a068a97f8fc3b1982e22e670c737ce6
- Size of remote file:
- 2.39 kB
- SHA256:
- 5ee29b2c04f14b7e432ad4d57ce7c58b5aa56f4fc5375cd8537091755f3932d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.