An autonomous differential evolution based on reinforcement learning for cooperative countermeasures of unmanned aerial vehicles

Zijian Cao, Kai Xu, Haowen Jia, Yanfang Fu, Chuan Heng Foh, Feng Tian

Abstract: Highlights•RL promotes individuals' autonomous decision-making by overcoming rule constraints.•RL enables the accumulation and learning of rewards information from individual evolution.•Historical information beneficial to individuals in autonomously selecting better and varied strategies.•Population backtracking enhances global search using historical rewards information.•Historical rewards information reflects the evolutionary progress of individuals.