摘要
针对现有大多协作式多智能体强化学习方法存在通信假设过于理想化问题,提出通信受限下的协作式多智能体强化学习方法。首先,通过引入随机信息丢失与高斯白噪声扰动,构建更贴近实际的通信受限环境;其次,提出一种基于残差连接的价值分解方法,利用残差结构增强系统对通信质量波动与观测噪声的鲁棒性;最后,在基于星际争霸多智能体挑战平台所构建的通信受限测试环境中对文中方法进行验证。实验结果表明:文中方法在多种通信受限场景下均表现优异,性能显著优于当前主流的多智能体强化学习方法。
To address the problem that most existing collaborative multi-agent reinforcement learning methods adopt overly idealized communication assumptions,a cooperative multi-agent reinforcement learning methods under communication constraints is proposed.First,a more realistic communication constrained environment 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 reinforcement learning methods.
作者
胡小亮
林雨婷
郭鹏程
黄世梅
陈叶旺
HU Xiaoliang;LIN Yuting;GUO Pengcheng;HUANG Shimei;CHEN Yewang(College of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210014,China;College of Computer Science and Technology,Huaqiao University,Xiamen 361021,China)
出处
《华侨大学学报(自然科学版)》
2026年第2期193-201,共9页
Journal of Huaqiao University(Natural Science)
基金
福建省厦门市产学基金资助项目(2024CXY0237)。
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
communication constraint
cooperative multi-agent reinforcement learning
residual connection
value decomposition
