Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
DemonAttackNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_demonattack_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_demonattack_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_demonattack_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b581625953d2cad4ed4fad7e252d6d8ebb0ac4dc45551ece2016b0c5d41f58f
- Size of remote file:
- 6.2 MB
- SHA256:
- 06804f130a217f422951cc9522c1ec7488e8b7cec7aa8cf2cab3472c937b33ed
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