Multi-UAV cooperative maneuver decision-making for pursuit-evasion using improved MADRL
Delin Luo, Zihao Fan, Ziyi Yang, Yang Xu
Abstract
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.