Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
QbertNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_qbert_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_qbert_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_qbert_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66f6949810bba2d00829c286e3a2432fb293400e258a95f636862472903bf743
- Size of remote file:
- 1.4 MB
- SHA256:
- 27d09f3c864fdbd837d796e6022464536b4eab1facb577e41b104098b8d63f6f
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