Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
SeaquestNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_seaquest_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_seaquest_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_seaquest_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed2d8ffbed615405ab79107a7951f621fc5eacea09d9c59eff1bf7f7a38df122
- Size of remote file:
- 7.01 MB
- SHA256:
- 354f4bf44b919f093b10fbbadac1bbcc11e5ad0412e4a436ae1c2166b7d2f04f
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