CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making

Zibin Dong, Yifu Yuan, Jianye HAO, Fei Ni, Yi Ma, Pengyi Li, YAN ZHENG

Keywords: Diffusion Models, Decision Making, Library, Reinforcement Learning, Imitation Learning

TL;DR: We present CleanDiffuser, the first diffusion model library specifically design for decision making.

Abstract: 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.

Submission Number: 1719