Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
SeaquestNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_seaquest_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_seaquest_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_seaquest_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5038b821d190d2f185d886db2fecaa4a0f8f539ca94125fd56648eb85ad27f3
- Size of remote file:
- 7.01 MB
- SHA256:
- c339c23053c07390c689e4733d7a4b794ff2145d3e944bf832733567e2b5ea45
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