Research on virtual entity decision model for LVC tactical confrontation of army units  

作　　者：GAO Ang GUO Qisheng DONG Zhiming TANG Zaijiang ZHANG Ziwei FENG Qiqi 

机构地区：[1]Military Exercise and Training Center,Army Academy of Armored Forces,Beijing 100072,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第5期1249-1267,共19页系统工程与电子技术（英文版）

基　　金：supported by the Military Scentific Research Project(41405030302,41401020301).

摘　　要：According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.

关 键 词：live-virtual-constructive(LVC) army unit tactical confrontation(TC) intelligent decision model multi-agent deep reinforcement learning 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] E91[自动化与计算机技术—计算机科学与技术]