Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
KrullNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_krull_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_krull_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_krull_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a6b299ac0f60e73a58d10ae93c7ce34887afb2a51753ca4fbff6705a1846c9b
- Size of remote file:
- 7.01 MB
- SHA256:
- 22b03311abeb6a65b0e6d4ea17991c9b86fd1651d75e31c90b5cabc3c493d535
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