Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
ChopperCommandNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_choppercommand_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_choppercommand_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_choppercommand_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91f36b1ca3553962abb14d92be1a6418271c4d96ea104be7b05f3516421be80d
- Size of remote file:
- 20.8 MB
- SHA256:
- c48d1c05a3a708c5e3a9a02f6470369a22fbfb5ebfffb6f46b7ca594957ca8be
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