Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
CentipedeNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_centipede_3333 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_centipede_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_centipede_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 514e5923e2d10d365d645cdfbc31c0b8b6df958cff8841d130a62660a8566646
- Size of remote file:
- 7.01 MB
- SHA256:
- 771300b27a8479119fc2b65bddcd15b6075d95311807408d345e8d6d1df86cd9
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