Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
AssaultNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_assault_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_assault_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_assault_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74abbad57ece8e054e101b5771d08aecd30ffcc424d3bc1d8e15bb59e0160560
- Size of remote file:
- 1.8 MB
- SHA256:
- 004d58be32a3cf3f0eb825e1ea8cc994277e29867eb964fe1c9666af183ea337
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