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:
- bdba72445bc117390b54e3f8e1781cd2bc7d31988170be27af6a9242f2177c1c
- Size of remote file:
- 20.2 MB
- SHA256:
- e28246d6347b4d607e7055fef34b99a994052ede6063e63d95fa226beb65beef
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