发表机构
The Ohio State University; University of Colorado Boulder; Pathfinder Applied Research, LLC（俄亥俄州立大学; 科罗拉多大学博尔德分校; Pathfinder应用研究有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用基础模型辅助多智能体强化学习，在共识式分布式架构中设计FM辅助的演员-评论家算法，以降低训练开销并提升无线随机接入网络优化的效率。
AI 中文摘要
随机接入（RA）是处理来自多个终端的不可预测数据流量的最基础的媒体访问控制（MAC）层调度方案之一。虽然多智能体强化学习（MARL）已被探索用于优化基于RA的无线网络，但其依赖于经验驱动的分布式策略学习，导致每个优化任务产生显著的训练开销，限制了其在实际应用中的可行性。在本工作中，我们提出利用基础模型（FM）来提高跨不同RA网络优化任务的MARL效率。具体而言，我们在基于共识的分布式MARL架构中设计了一种FM辅助的演员-评论家算法，并提供了在局部奖励交换和非线性值函数逼近下的收敛性分析，以表明我们的算法实现了与传统MARL（具有评论家模型交换和线性逼近）相同的收敛阶。我们的数值结果表明，我们基于FM的方法显著提高了RA网络优化的MARL速度。
英文摘要
Random access (RA) is one of the most foundational medium access control (MAC) layer scheduling schemes for handling unpredictable data traffic from multiple terminals. While multi-agent reinforcement learning (MARL) has been explored to optimize RA-based wireless networks, its reliance on experience-driven, distributed policy learning incurs significant training overhead for each optimization task, limiting its feasibility in real-world applications. In this work, we propose to leverage a foundation model (FM) to improve MARL efficiency across diverse RA network optimization tasks. Specifically, we design an FM-aided actor-critic algorithm within a consensus-based decentralized MARL architecture and provide its convergence analysis under local reward exchanges and nonlinear value function approximations to show that our algorithm achieves the same convergence order as the conventional MARL with critic model exchanges and linear approximations. Our numerical results show that our FM-based approach significantly enhances MARL speed for RA network optimization.
CommentsThis paper has been accepted in ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) 2026
