Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
KangarooNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_kangaroo_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_kangaroo_3333 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_kangaroo_3333 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aafb2442100ea925ac6452d96fbc1daee2e7c033aebc20c2c8b782a9bff50db9
- Size of remote file:
- 7.01 MB
- SHA256:
- ee1be1f3aad3f80e821d2480ac98041f6a9636ba8d26ca62ec76990f8cd6458a
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