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.

翻译：利用扩散模型强大的生成能力构建决策智能体已取得广泛成功。然而，目前仍缺乏一个易用、模块化的开源库，为基于扩散模型的决策算法提供定制化且高效的开发支持。本工作介绍了CleanDiffuser，这是首个专门为决策算法设计的扩散模型库。通过重新审视扩散模型在决策领域中的作用，我们识别出一组构成CleanDiffuser核心的基础子模块，使得仅需简单灵活的基础构件即可实现各类扩散模型算法。为验证CleanDiffuser的可靠性与灵活性，我们在广泛的任务范围内对基于CleanDiffuser实现的各种扩散模型算法进行了全面评估。分析性实验提供了大量有价值的设计选择与洞见，揭示了机遇与挑战，并为未来研究奠定了坚实基础。CleanDiffuser将为决策研究社区提供长期支持，以提升可复现性并促进更鲁棒解决方案的发展。CleanDiffuser的代码与文档已开源，详见 https://github.com/CleanDiffuserTeam/CleanDiffuser。