Multi-Agent and Multi-Constraint Combat Task Allocation Method Based on EC-MAVEN Algorithm
To solve the problems of slow convergence and low exploration efficiency of existing heu-ristic algorithms in the face of multi-constraint combat task allocation.Based on the combat simulation environment,an improved multi-intelligent agent variational exploration method(EC-MAVEN)is pro-posed to model the combat task assignment problems with multiple constraints,and to improve the explora-tion efficiency of intelligent agents in complex tasks,and the control of scenes is used to improve the utili-zation rate of good samples and to speed up the training speed,and to facilitate the jumping out of the local optimum.The simulation example is verified by the combat strike task allocation environment,and the results show that the EC-MAVEN algorithm can reduce falling into local sub-optimality,and can shorten the training time,and to improve the mission success rate.
国家科技期刊平台
NSTL
万方数据
