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:
- e7f045d0698e9440da2eefbd616a4431b33a9eae6762b5b260dfe7a9954adf91
- Size of remote file:
- 6.18 MB
- SHA256:
- 4410e37b49d8964522ef5460e03d05e7b4e663ca5af8937dd7027bcca03bd1b3
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