Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
SkiingNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_skiing_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_skiing_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_skiing_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e77bcd449fcc7d59ef00e0e678ae755164847162ab06f0f1adb66fb7fcadd95
- Size of remote file:
- 6.98 MB
- SHA256:
- 9888f49db67927f5ac42a33aa469d177ea4b862fcbe1c8861b4a1fc6ae240418
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.