Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
PitfallNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_pitfall_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_pitfall_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_pitfall_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 441e35acdc955fabb4dae4e3ad5f19f6bb0af34fa7dd988f1ad8e6e540648655
- Size of remote file:
- 7.01 MB
- SHA256:
- f4fd3e64f3a8d24f1390ef1c3ae853ba9b397b7b5f3b99a4691a9b8a992f7e52
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