Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
AssaultNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_assault_2222 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_assault_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_assault_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37f1e3fd400e73dca0bece8fa1ec8af3309b6742ac20468ec287b481815561ba
- Size of remote file:
- 6.98 MB
- SHA256:
- f45cac517e4284e8896065eeaeae04b04d448cc7cbe663123a5481f6b1cc5997
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.