  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.
