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:
- f4a4084a18043a3ae8a36e6994463e891133bd0767f7dbf6e64022b76f772f28
- Size of remote file:
- 20.8 MB
- SHA256:
- 8e798110e09d1d7c1060c6526c1f5578fee98bfdc99193db0f9701e18805fe05
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