Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
ChopperCommandNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_choppercommand_2222 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_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_choppercommand_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f77eb8b8bcf10a55e1d778cf4a19b7698bf843c4f245cbd7a871c4e4f1436d57
- Size of remote file:
- 20.8 MB
- SHA256:
- dcdd7634292b84764c19441b69c3decb74b53f4ea941e63d6dc00aa9934d93d5
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