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:
- 27a267c0eae6cd1caedef451c66082c51454c3b73b24e2d3e34bd021c1d1bd3a
- Size of remote file:
- 20.8 MB
- SHA256:
- 23bf4021cd0e30ed1e86d7357c6b8e551f8b278d377614f7c2da5f1afdd73c37
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