Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
DefenderNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_defender_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_defender_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_defender_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80f6e6cdf4280b48e36e937d5f330158eb568a36b1f61a2ab77a2cddaa4b22c2
- Size of remote file:
- 7.01 MB
- SHA256:
- 88105d5adb38593e72bbf69d46de336acac90c1a1fc0da5f7177c7a08ed81360
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