NetWorld: Communication-Based Diffusion World Model for Multi-Agent Reinforcement Learning in Wireless Networks

Abstract

As wireless communication networks grow in scale and complexity, diverse resource allocation tasks become increasingly critical. Multi-Agent Reinforcement Learning (MARL) provides a promising solution for distributed control, yet it often requires costly real-world interactions and lacks generalization across diverse tasks. Meanwhile, recent advances in Diffusion Models (DMs) have demonstrated strong capabilities in modeling complex dynamics and supporting high-fidelity simulation. Motivated by these challenges and opportunities, we propose a Communication-based Diffusion World Model (NetWorld) to enable few-shot generalization across heterogeneous MARL tasks in wireless networks. To improve applicability to large-scale distributed networks, NetWorld adopts the Distributed Training with Decentralized Execution (DTDE) paradigm and is organized into a two-stage framework: (i) pre-training a classifier-guided conditional diffusion world model on multi-task offline datasets, and (ii) performing trajectory planning entirely within this world model to avoid additional online interaction. Cross-task heterogeneity is handled via shared latent processing for observations, two-hot discretization for task-specific actions and rewards, and an inverse dynamics model for action recovery. We further introduce a lightweight Mean Field (MF) communication mechanism to reduce non-stationarity and promote coordinated behaviors with low overhead. Experiments on three representative tasks demonstrate improved performance and sample efficiency over MARL baselines, indicating strong scalability and practical potential for wireless network optimization.