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原文摘要

In wireless communication systems, efficient and adaptive resource allocation is essential for enhancing overall Quality of Service (QoS). Compared with conventional Model-Free Reinforcement Learning (MFRL), Model-Based RL (MBRL) first learns a generative world model for subsequent planning, enabling more stable training through the reuse of historical experience. However, deploying MBRL in large-scale wireless networks remains challenging due to high-dimensional stochastic dynamics, strong inter-agent cooperation, and stringent communication constraints. To overcome these challenges, we propose a Multi-Agent Conditional Diffusion Model Planner (MA-CDMP) for decentralized communication resource management. Following the Decentralized Training with Decentralized Execution (DTDE) paradigm, MA-CDMP models each communication node as an autonomous agent and employs Diffusion Models (DMs) to capture environment dynamics. An inverse dynamics model is further incorporated to guide action generation, improving sample efficiency and policy scalability. Moreover, to efficiently approximate large-scale agent interactions, we introduce a Mean Field (MF) mechanism to assist the classifier in conditional DMs. This mitigates inter-agent non-stationarity and enhances cooperation with minimal communication overhead. We further establish an upper bound on the distributional approximation error induced by MF-based diffusion generation, guaranteeing convergence stability and reliable modeling of multi-agent stochastic dynamics. Extensive experiments demonstrate that MA-CDMP consistently outperforms existing MARL baselines in terms of average reward and QoS metrics, showcasing its scalability and practicality for real-world wireless network optimization.