CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making (2406.09509v2)

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. The code and documentation of CleanDiffuser are open-sourced on the https://github.com/CleanDiffuserTeam/CleanDiffuser.

• The paper introduces a modular open-source library that decouples diffusion model components for flexible decision-making algorithms.
• It features customizable network architectures and guided sampling methods to support various decision strategies, backed by rigorous benchmarking.
• Comprehensive evaluations across 37 reinforcement and imitation learning environments validate the library’s effectiveness and scalability.

Modularized Library for Diffusion Models in Decision Making

This paper introduces a modularized open-source library aimed at facilitating the development and evaluation of diffusion models (DMs) applied in decision-making tasks. While DMs have demonstrated promising generative capabilities across various domains, there remains a need for a tailored library that simplifies the creation and customization of DM-based decision algorithms.

Overview and Contributions

The authors present a library with a modular architecture specifically designed to leverage DMs for decision-making scenarios. This library consists of essential sub-modules, enabling a comprehensive and flexible implementation landscape. The core contributions include:

1. Decoupled Diffusion Models: The library implements diffusion models with separate core components for SDE/ODE and solvers, allowing users to experiment with different solvers and sampling strategies without additional overhead after training.
2. Network Architectures and Guided Sampling: A variety of network architectures are implemented to support different algorithmic requirements. The library also decouples guided sampling methods to ensure flexibility in handling diverse decision-making conditions and perceptual inputs.
3. User-Friendly Pipeline Configuration: With the library's structure, algorithms can be assembled akin to building blocks, facilitating straightforward experimentation and customization by researchers.
4. Comprehensive Benchmarking: The authors conduct extensive evaluations across multiple environments, leveraging tens of thousands of GPU hours to benchmark various DM algorithms. This effort provides robust references and insights for future research.

Practical and Theoretical Implications

The library's design supports the application of DMs in three primary roles: planners, policies, and data synthesizers. By enabling long-term trajectory generation and policy modeling with detailed architectural flexibility, the library addresses unique demands in decision-making tasks not previously covered by existing diffusion libraries.

Strong Numerical Results

Through rigorous benchmarking, the authors demonstrate the reliability and flexibility of their library. Experiments were conducted across 37 RL and IL environments for nine algorithms, offering extensive quantitative analyses that reveal both opportunities and challenges in using DM-based decision solutions.

Discussion and Future Directions

While the library marks a significant step toward broader adoption and experimentation of DMs in decision-making, several avenues remain open for exploration. The library provides insights into the impacts of diffusion backbones and sampling steps, yet the concept of sampling degradation warrants further investigation to optimize performance. Moreover, understanding the nuances between SDE and ODE could refine algorithmic choices.

The library's modular design and comprehensive feature set lay a foundation for future developments. It can significantly enhance reproducibility, catalyze innovation in algorithm design, and potentially scale the application of DMs across various AI domains.

In conclusion, this work fills a critical gap by providing a structured, flexible platform for researchers to develop, test, and refine DM applications in decision-making. As AI continues to evolve, such tools will be invaluable in driving forward the integration of diffusion models into real-world decision-making scenarios.