Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BreakoutNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_breakout_3333 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_breakout_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_breakout_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4be6e08b5a595748e5745b81ad0286eb5b1d888953dfcbb5d50c670846f6b6b4
- Size of remote file:
- 6.98 MB
- SHA256:
- a34818ab63ce5ba34a0137f96cb123da368c9c0e610e0722ba0c6003a56ca3ef
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