Leveraging the powerful generative capability of diffusion models (DMs) to build decision-making agents has achieved extensive success. However, there is still a demand for an easy-to-use and modularized open-source library that offers customized and efficient development for DM-based decision-making algorithms. In this work, we introduce CleanDiffuser, the first DM library specifically designed for decision-making algorithms. By revisiting the roles of DMs in the decision-making domain, we identify a set of essential sub-modules that constitute the core of CleanDiffuser, allowing for the implementation of various DM algorithms with simple and flexible building blocks. To demonstrate the reliability and flexibility of CleanDiffuser, we conduct comprehensive evaluations of various DM algorithms implemented with CleanDiffuser across an extensive range of tasks. The analytical experiments provide a wealth of valuable design choices and insights, reveal opportunities and challenges, and lay a solid groundwork for future research. CleanDiffuser will provide long-term support to the decision-making community, enhancing reproducibility and fostering the development of more robust solutions. The code and documentation of CleanDiffuser are open-sourced on the https://github.com/CleanDiffuserTeam/CleanDiffuser.
@article{arxiv.2406.09509,
title = {CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making},
author = {Zibin Dong and Yifu Yuan and Jianye Hao and Fei Ni and Yi Ma and Pengyi Li and Yan Zheng},
journal= {arXiv preprint arXiv:2406.09509},
year = {2024}
}
Comments
Accept by NeurIPS2024 Datasets and Benchmarks Track. The first two authors contribute equally to this work. Code and documentation: https://github.com/CleanDiffuserTeam/CleanDiffuser