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

• URL: http://arxiv.org/abs/2502.18549v1
• Date: Tue, 25 Feb 2025 16:05:33 GMT
• Title: Target Defense with Multiple Defenders and an Agile Attacker via Residual Policy Learning
• Authors: Jiyue Tao, Tongsheng Shen, Dexin Zhao, Feitian Zhang,
• Abstract summary: This letter focuses on a particularly challenging scenario in which the attacker is more agile than the defenders.<n>We propose a novel residual policy framework that integrates deep reinforcement learning with the force-based Boids model.<n>In this framework, the Boids model serves as a baseline policy, while DRL learns a residual policy to refine and optimize the defenders' actions.
• Score: 0.918715978278858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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