Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
KrullNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_krull_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_krull_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_krull_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a8c4f6969b849897a06083adcea8715dbba6451ef59aea0b442415d1a9b451f
- Size of remote file:
- 7.01 MB
- SHA256:
- 37c821897f3919d5c74f03429350c758fe25e0f2769d44a5174d4a83b921fe85
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