Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
QbertNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_qbert_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_qbert_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_qbert_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e6fdc86dfed182b4cbe7e23c3a95421dbcef3e1a9c97694610fe1b51d9372e3
- Size of remote file:
- 6.98 MB
- SHA256:
- c0de3511ef95a5e869b1b4e6c32433d0a6f0b58268f2b91ba71b27c7a320ce95
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