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.
View on arXiv@article{tao2025_2502.18549,
title={ Target Defense with Multiple Defenders and an Agile Attacker via Residual Policy Learning },
author={ Jiyue Tao and Tongsheng Shen and Dexin Zhao and Feitian Zhang },
journal={arXiv preprint arXiv:2502.18549},
year={ 2025 }
}