无人机蜂群防御中决策级拦截优先级排序的强化学习
Reinforcement Learning for Decision-Level Interception Prioritization in Drone Swarm Defense
AI总结：
针对无人机蜂群防御中的拦截优先级决策问题，本文提出在离散动作空间中使用强化学习智能体协调多效应器，并通过高保真仿真验证其相比规则基线的更低损害与更高防御效率。
摘要：
低成本自杀式无人机蜂群日益增长的威胁对现代防御系统构成了严峻挑战，要求系统能够快速且战略性地做出决策，以在多个效应器和高价值目标区域之间确定拦截优先级。在本工作中，我们展示了一个案例研究，证明了强化学习在应对这一挑战中的实际优势。我们引入了一个高保真仿真环境，该环境捕捉了现实中的作战约束，在此环境中，一个决策级强化学习智能体学习协调多个效应器以实现最优的拦截优先级排序。该智能体在离散动作空间中运行，根据观测到的状态特征（如位置、类别和效应器状态）为每个效应器选择要拦截的无人机。我们在数百个模拟攻击场景中，将学习到的策略与手工设计的基于规则的基线进行了评估。基于强化学习的策略在保护关键区域方面始终实现了更低的平均损害和更高的防御效率。本案例研究凸显了强化学习作为防御架构中战略层的潜力，它能在不取代现有控制系统的前提下增强系统韧性。所有代码和仿真资产均已公开发布以实现完全可复现性，并且有一个视频演示展示了该策略的定性行为。
英文摘要：
The growing threat of low-cost kamikaze drone swarms poses a critical challenge to modern defense systems demanding rapid and strategic decision-making to prioritize interceptions across multiple effectors and high-value target zones. In this work, we present a case study demonstrating the practical advantages of reinforcement learning in addressing this challenge. We introduce a high-fidelity simulation environment that captures realistic operational constraints, within which a decision-level reinforcement learning agent learns to coordinate multiple effectors for optimal interception prioritization. Operating in a discrete action space, the agent selects which drone to engage per effector based on observed state features such as positions, classes, and effector status. We evaluate the learned policy against a handcrafted rule-based baseline across hundreds of simulated attack scenarios. The reinforcement learning based policy consistently achieves lower average damage and higher defensive efficiency in protecting critical zones. This case study highlights the potential of reinforcement learning as a strategic layer within defense architectures, enhancing resilience without displacing existing control systems. All code and simulation assets are publicly released for full reproducibility, and a video demonstration illustrates the policy's qualitative behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
