Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
FrostbiteNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_frostbite_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_frostbite_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_frostbite_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57b9039666d92e65acf47dd17222b34dbec3533e17790c1082f5a5194fabeee3
- Size of remote file:
- 7.01 MB
- SHA256:
- 5d598b9bdf09c5ff36114646b2e3c0bfc8a605d1fa35684fb4bb6fe5e679a9fd
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