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:
- 3620bf8fb44577f21ffe58815f25d53158783f2e2c7f91e3e570f081a6b6773b
- Size of remote file:
- 1.25 MB
- SHA256:
- 6114e85351a7e84b3bf614e739398a778d5abb19da3e1c2b36989d23d8542d33
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