Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
AssaultNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_assault_3333 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_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_assault_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2191481b044cef6557753f2ac2e8ec0f0762642ea3fb88aa4a17fb30ab722ca
- Size of remote file:
- 6.98 MB
- SHA256:
- 4889fb70994d6c59fbddbb9baa47acb4d8c12b0941e0e210d81199e1e2c94410
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