华侨大学学报（自然科学版）2026，Vol.47Issue(2)：193-201,9.DOI:10.11830/ISSN.1000-5013.202511012
通信受限下的协作式多智能体强化学习方法
Cooperative Multi-Agent Reinforcement Learning Methods Under Communication Constraints
摘要
Abstract
To address the problem that most existing collaborative multi-agent reinforcement learning meth-ods adopt overly idealized communication assumptions,a cooperative multi-agent reinforcement learning meth-ods under communication constraints is proposed.First,a more realistic communication constrained environ-ment is constructed by introducing random information loss and additive Gaussian white noise disturbance.Then,a residual connection-based value decomposition method is proposed,leveraging residual structures to enhance the robustness of system against communication quality fluctuations and observational noise.Finally,the proposed method is validated in a communication constrained test environment built on the StarCraft multi-agent challenge benchmark.Experimental results show that the proposed method performs excellently under various communication-constrained scenarios,significantly outperforming current mainstream multi-agent rein-forcement learning methods.关键词
通信受限/协作式多智能体强化学习/残差连接/价值分解Key words
communication constraint/cooperative multi-agent reinforcement learning/residual connection/value decomposition分类
信息技术与安全科学引用本文复制引用
胡小亮,林雨婷,郭鹏程,黄世梅,陈叶旺..通信受限下的协作式多智能体强化学习方法[J].华侨大学学报（自然科学版）,2026,47(2):193-201,9.基金项目
福建省厦门市产学基金资助项目(2024CXY0237) （2024CXY0237）
