Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
AmidarNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_amidar_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_amidar_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_amidar_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b284b298458c594a05054fefcf2ed7f8ecf697b59b381276e2f8b92a4f8cbe7
- Size of remote file:
- 6.99 MB
- SHA256:
- 3fa5251672767807f74935e07afae86f59af2a9845d2b32a17710ce79d2ab165
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