高技术通讯2024，Vol.34Issue(3)：256-264,9.DOI:10.3772/j.issn.1002-0470.2024.03.004
结合先验知识的多智能体博弈对抗研究
Research on multi-agent game confrontation combined with prior knowledge
摘要
Abstract
The complex adversarial environment without real-time reward is the current research hot spot in the field of deep reinforcement learning(DRL).In such environment,the use of deep reinforcement learning algorithm alone in general leads to a lower convergence speed and unsatisfactory performance.In this regard,this paper proposes an intelligent game process framework based on the combination of prior knowledge and deep reinforcement learn-ing,and designs three modules of data processing,enhancement mechanism and action decision-making to improve both the convergence speed and the countermeasure effect under complex confrontation environment through three enhancement mechanisms including threat assessment,task scheduling and loss ratio.The simulation results on the DataCastle(DC)platform show that the agent trained by the proposed intelligent game process framework has a fast convergence speed and higher winning rate than the agent only based on deep reinforcement learning.关键词
智能博弈/先验知识/深度强化学习(DRL)/威胁评估/任务调度Key words
intelligent game/prior knowledge/deep reinforcement learning(DRL)/threat estimation/task dispatch引用本文复制引用
袁婷帅,冯宇,李永强..结合先验知识的多智能体博弈对抗研究[J].高技术通讯,2024,34(3):256-264,9.基金项目
国家自然科学基金(61973276)资助项目. （61973276）
