Target Defense with Multiple Defenders and an Agile Attacker via Residual Policy Learning

作     者：Tao, Jiyue Shen, Tongsheng Zhao, Dexin Zhang, Feitian 

作者机构：Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering and the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China National Innovation Institute of Defense Technology Beijing100071 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2025年

核心收录：

主　　题：Adversarial machine learning 

摘      要：The target defense problem involves intercepting an attacker before it reaches a designated target region using one or more defenders. This letter focuses on a particularly challenging scenario in which the attacker is more agile than the defenders, significantly increasing the difficulty of effective interception. To address this challenge, we propose a novel residual policy framework that integrates deep reinforcement learning (DRL) with the force-based Boids model. In this framework, the Boids model serves as a baseline policy, while DRL learns a residual policy to refine and optimize the defenders’ actions. Simulation experiments demonstrate that the proposed method consistently outperforms traditional interception policies, whether learned via vanilla DRL or fine-tuned from force-based methods. Moreover, the learned policy exhibits strong scalability and adaptability, effectively handling scenarios with varying numbers of defenders and attackers with different agility levels. Copyright © 2025, The Authors. All rights reserved.