Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
KangarooNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_kangaroo_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_kangaroo_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_kangaroo_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7efe1c52e56951907dcfde9dadba832a24d5cf7b6f2c25642b71368081d3c172
- Size of remote file:
- 7.01 MB
- SHA256:
- 78dd8056922584908fbf19c46266865b1952ac48e90b02d81912ffb0449ae8cc
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