Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
SpaceInvadersNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_spaceinvaders_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_spaceinvaders_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_spaceinvaders_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84448f8c0ce48c7fb12e4cde74407f99a3e7f84a2b25bb3d7483578756eedc57
- Size of remote file:
- 6.98 MB
- SHA256:
- 5887fb7658318f3508a57ca2d0cdce9672e980ea0b805f52c3c340bd34560a14
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