Reinforcement Learning
sample-factory
TensorBoard
deep-reinforcement-learning
CentipedeNoFrameskip-v4
Eval Results (legacy)
Instructions to use edbeeching/atari_2B_atari_centipede_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_centipede_1111 with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r edbeeching/atari_2B_atari_centipede_1111 -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 113406f289f3cdae5dc6872d9260af868629d8a715d0cff2cacc407b3d2f29e8
- Size of remote file:
- 7.01 MB
- SHA256:
- afc5f8f85ad3f51b071e6148ab6a8e82fb0a35d144277cbe92ea8ccc6327d069
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.