Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
DefenderNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_defender_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_defender_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_defender_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eeafaec439495f20a6bdf4d3e1547a9b7f94c52f47b8205d61a1d4e5c05f2b47
- Size of remote file:
- 7.01 MB
- SHA256:
- 0fcc0d70129a4c1bf2e124af3c54e1fc16b5760f3ada5da13344b718db62d756
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