Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
ChopperCommandNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_choppercommand_3333 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_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_choppercommand_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e95bc6c3933531af006d1e46fa2d40817fb8d1f98a47415f6158b396447001d
- Size of remote file:
- 7.01 MB
- SHA256:
- adfb70ec12e105c634934021ac5c647b1fe6e87d5fef885dff18b6bc02e1def4
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