Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
BoxingNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_boxing_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_boxing_2222 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_boxing_2222 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6113ca2936ca8513426c7ff1eadaf4230c7da843e8b3ddca49d92327d4914a8
- Size of remote file:
- 7.01 MB
- SHA256:
- 34ee2a33f7cc6b7b939285e7d6acb848e3312bdb919dbabad09f23b1ee6994b2
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