基于深度强化学习算法的多无人水面航行器编队构造  

Formation construction of multiple unmanned surface vehicles based on deep reinforcement learning algorithm

作　　者：关巍[1] 张诚 崔哲闻 韩虎生 GUAN Wei;ZHANG Cheng;CUI Zhewen;HAN Husheng(Navigation College,Dalian Maritime University,Dalian 116026,China;Department of Network and Internet of Things Engineering,School of Computer and Sofiware,Dalian Neusoft University of Information,Dalian 116031,China)

机构地区：[1]大连海事大学航海学院,辽宁大连116026 [2]大连东软信息学院计算机与软件学院网络与物联网工程系,辽宁大连116031

出　　处：《大连海事大学学报》2025年第1期11-20,42,共11页Journal of Dalian Maritime University

基　　金：国家自然科学基金资助项目(52171342)。

摘　　要：针对传统多智能体深度确定性策略梯度算法(MADDPG)收敛速度较慢的问题,本文通过在值函数阶段引入注意力机制来提升多无人水面航行器系统编队决策模型的收敛速度,并通过编队模型与编队避碰和编队构造奖励函数的配合,提升了多无人水面航行器完成编队构造任务的效率。仿真结果验证了本文方法可有效完成多种环境下的多无人水面航行器编队构造任务,为未来多无人船编队构造应用提供了理论研究基础。With the rapid development of science and technology,multi-unmanned ship systems have shown great potential in military,rescue and escort mission scenarios.The purpose of this paper is to explore the formation construction problem of multiple unmanned surface vehicle systems based on multi-agent deep reinforcement learning algorithm.Considering the sluggish convergence speed of the conventional multi-agent deep deterministic policy gradient algorithm(MADDPG),this study incorporates the attention mechanism into the value function stage to enhance the convergence speed of the formation decision model for a multi-UAV system.Through the cooperation of the formation model of the unmanned surface vehicle with the formation collision avoidance and the formation construction reward function,the efficiency of the multi-UAV to complete the formation construction task is finally improved.The simulation results conclusively demonstrate the efficacy of the proposed method in accomplishing multi-unmanned surface vehicle formation construction tasks,thereby establishing a solid theoretical foundation for future applications of multi-unmanned ship formation construction.

关 键 词：多无人水面航行器 编队构造 MADDPG算法 深度强化学习 注意力机制 

分 类 号：U664.82[交通运输工程—船舶及航道工程] TP24[交通运输工程—船舶与海洋工程]