Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
FrostbiteNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_frostbite_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_frostbite_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_frostbite_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46a9f21ddd588898a8458720856cb16bb3705c3841345d28e9711a3765208fcd
- Size of remote file:
- 7.01 MB
- SHA256:
- e1db7af617fec0b4c465900fbc633319c46a8ae7f8e2a9e1a8b5a8951db0a27c
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