Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
SpaceInvadersNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_spaceinvaders_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_spaceinvaders_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_spaceinvaders_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05f0ec9291d7d458fbe745777dcdbda3ab9a9424c9ea713afa08ca0aacbc2c17
- Size of remote file:
- 6.98 MB
- SHA256:
- f21d686d9adbcd04f88fc83b1538bd770145ef3780941a719e27a7a7922f9287
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