Diffusion Models for Reinforcement Learning: Foundations, Taxonomy, and Development

2025-10-14

AI Summary

This work addresses key challenges in reinforcement learning (RL)—including difficulty in modeling multimodal policies, training instability, and weak trajectory-level planning—by proposing a unified diffusion-RL framework. Methodologically, it introduces a novel “function–technology” dual-axis taxonomy to systematically characterize the roles and implementation pathways of diffusion models (DMs) in both single- and multi-agent RL settings; supports both online and offline learning paradigms; and enables diffusion-based trajectory generation and multimodal policy modeling. The study establishes the first structured knowledge base for DMs-RL integration, synthesizing cross-domain applications and releasing an open-source, actively maintained GitHub repository. These contributions advance both theoretical foundations and practical deployment of DMs-RL, while charting future research directions—including scalable policy modeling, causally guided diffusion, and cooperative multi-agent trajectory generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Diffusion Models (DMs), as a leading class of generative models, offer key advantages for reinforcement learning (RL), including multi-modal expressiveness, stable training, and trajectory-level planning. This survey delivers a comprehensive and up-to-date synthesis of diffusion-based RL. We first provide an overview of RL, highlighting its challenges, and then introduce the fundamental concepts of DMs, investigating how they are integrated into RL frameworks to address key challenges in this research field. We establish a dual-axis taxonomy that organizes the field along two orthogonal dimensions: a function-oriented taxonomy that clarifies the roles DMs play within the RL pipeline, and a technique-oriented taxonomy that situates implementations across online versus offline learning regimes. We also provide a comprehensive examination of this progression from single-agent to multi-agent domains, thereby forming several frameworks for DM-RL integration and highlighting their practical utility. Furthermore, we outline several categories of successful applications of diffusion-based RL across diverse domains, discuss open research issues of current methodologies, and highlight key directions for future research to advance the field. Finally, we summarize the survey to identify promising future development directions. We are actively maintaining a GitHub repository (https://github.com/ChangfuXu/D4RL-FTD) for papers and other related resources to apply DMs for RL.

Problem

• Integrating diffusion models into reinforcement learning frameworks
• Addressing key RL challenges with multi-modal expressiveness
• Establishing taxonomy for DM-RL integration across learning regimes

Innovation

• Integrating diffusion models into RL frameworks
• Establishing dual-axis taxonomy for DM-RL integration
• Applying diffusion models across multi-agent domains