Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
HeroNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_hero_1111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_hero_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_hero_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 986f3f1d4aeb220ecf9eefd9d8e150535ed2ea2a8a1eb00c52f496de2eccd818
- Size of remote file:
- 7.01 MB
- SHA256:
- 917ca8db77c1f265a0fd61a328158f791e6cb140dc543f8d630818d97c5ca948
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.