Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BattleZoneNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_battlezone_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_battlezone_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_battlezone_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 215717f833b698c70b88eed35a5e707f78243289f97f8625ac426ab5aab48856
- Size of remote file:
- 2.46 MB
- SHA256:
- 53125539245c39b283ac17257540e41ad643a6f3044bb7d56119f9149ab2664a
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