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:
- 99c394a5e063b12003e5321dfdc72c636bb7db0a7a6ee4edd0401fdaa0ee9e4f
- Size of remote file:
- 20.8 MB
- SHA256:
- 8af1d6d3108d1a4436c8affa347972c11031a0f6bb6f1f46558538c03cad2e53
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