Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
DemonAttackNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_demonattack_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_demonattack_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_demonattack_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eea7072c664743aafa6e1d1541f4f8c2f7f8b39f7d396ee52ea17b18b71d410b
- Size of remote file:
- 6.98 MB
- SHA256:
- 9bbf30c2c04055c266ee9f737380c3f430c705c9225c0ea429be11cd0776f611
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