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:
- 792bdf6ac8aa9cc2fafd07e91e29b28383abab945a09ed8c0c49729332ab4129
- Size of remote file:
- 20.8 MB
- SHA256:
- 83cc43a0fa68df526fb980a13be8c6b78459c522dadff9f8ee6b20ed81af3886
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