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:
- 946f835e6d56047c8fde39ab55943c7bd1e4a5c7fb05b3d392ebd618680da6f6
- Size of remote file:
- 7.01 MB
- SHA256:
- 6a73a019187da55e3787a6156528d095ff265b75a942515d2a985d138340d368
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