Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
RiverraidNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_riverraid_1111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use edbeeching/atari_2B_atari_riverraid_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_riverraid_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e65ca4ceec3ce5415aa21908107aaeac85d014024f489d786deb00c92c3d1ab
- Size of remote file:
- 20.8 MB
- SHA256:
- f772400dbe1a7777b56b19993bf8db6250ff6ba57849e0d31f2b27d9de75e339
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