Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
IceHockeyNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_icehockey_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_icehockey_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_icehockey_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e57080f6a3fcd584144ad48c0d240d592671d840a1b433bf02cebd00d525c22
- Size of remote file:
- 20.8 MB
- SHA256:
- 21153e7890596d867ebb59e2d4abc51aa3bcf4f01d7259cd81a066ab2f05f3d3
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