Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
AssaultNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_assault_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_assault_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_assault_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 376434397070e5980ec8ef4c1af4b46c675a336c366ef5ccac8cd1378361d1f2
- Size of remote file:
- 6.98 MB
- SHA256:
- ebc8bba5e0675038d9edf9a2324bcdb8d59bf1358c3be08e060e143e0dc4d8ce
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