Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Abhinandan/Atari with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Abhinandan/Atari with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Abhinandan/Atari", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 019f553f6cfeda8d704574207d52dc36f3cbbcd479344dd05215708dfa64d745
- Size of remote file:
- 276 kB
- SHA256:
- 25fcb8f4cd387bbd8ea8f1869dda23f9bc38859fa045e4ba505c96295aab752b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.