Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BerzerkNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_berzerk_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_berzerk_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_berzerk_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb2bbfb68b9c0990cbc1e07696b7f663b121f5b747ec67289509e9c54dd6d436
- Size of remote file:
- 7.01 MB
- SHA256:
- 775792abda7b4a536427caaa1299336e8983a82ab23461b9d0b3a6a73d3fa4e4
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