强化学习
计算机科学
芯(光纤)
图层(电子)
追逃
人工智能
逃避(道德)
频道(广播)
数学优化
计算机网络
电信
数学
化学
免疫系统
有机化学
免疫学
生物
作者
Delin Luo,Zihao Fan,Ziyi Yang,Yang Xu
标识
DOI:10.1016/j.dt.2023.11.013
摘要
Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning (MADRL) is proposed. In this method, an improved CommNet network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit (GRU) is added to the actor-network structure to remember historical environmental states. Subsequently, another GRU is designed as a communication channel in the CommNet core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.
科研通智能强力驱动
Strongly Powered by AbleSci AI