Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BerzerkNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_berzerk_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_berzerk_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_berzerk_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f28036729d99bec0a412d288944aefca2876180d84aebfdc1a5240f0cc77c55e
- Size of remote file:
- 4.14 MB
- SHA256:
- 4b8b116c56aa9b1f81f2c74d38fc9e72b405b8bba845ed962117652882aea990
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