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:
- 2df1a253e5aa24e27a9cd125eaf992e9aaa3276687abb4c2c0f4a64aef0cda68
- Size of remote file:
- 20.8 MB
- SHA256:
- 854cf54441bcb93069a05ac35ac5ab5487ae2722b2ba33e9bc598da994a67cf4
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