Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
KrullNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_krull_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_krull_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_krull_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1d5a758e4db4c2ad3942babd67c1e122ead525f25c2434ea954ac4210911e0b
- Size of remote file:
- 7.01 MB
- SHA256:
- cf6f39cb8a4607f81d0ddb25de98246de4e3efddc5c3d31eac514039d2a8e805
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