Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
SkiingNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_skiing_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_skiing_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_skiing_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c8d431a7e4bb9abec5514a411f830d202634007fb955061b3d05d3738af855d
- Size of remote file:
- 6.98 MB
- SHA256:
- 46f1b001d309b97864c58feb40d25acb260e7b411a38c9435fbbb0aa16b17814
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