在分布式无人机群中发现奖励目标 使用物理信息的多代理反向增强学习
IEEE transactions on cybernetics
|March 3, 2025
概括
这项研究介绍了一种基于物理的多代理反向增强学习 (PI-MAIRL) 方法,以了解无人机群的行为. PI-MAIRL推断了控制目标和网络拓,增强了群集智能和安全应用.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 控制系统工程 控制系统工程
- 网络科学 网络科学
背景情况:
- 无人机群体表现出复杂的合作行为,对服务和国家安全构成风险.
- 了解群控制目标和无人机间通信对于管理这些风险至关重要.
- 目前的方法在复杂的无人机群动力学中难以破译封闭的控制目标.
研究的目的:
- 开发一种新的方法,从观测数据中推断无人机群控制目标.
- 使用基于物理的模型揭示无人机群的网络拓.
- 增强对群体行为的理解,以改善模仿学习和体验推断.
主要方法:
- 提出了一个基于物理的多代理反向增强学习 (PI-MAIRL) 算法.
- 从观测数据中推断奖励功能 (控制目标).
- 利用基于物理的无人机动态模型来发现网络拓.
主要成果:
- PI-MAIRL在物理不结合的群群场景中成功地推断了控制目标和网络拓.
- 与无模型的反增强学习算法相比,基于物理的方法提供了更好的计算效率.
- PI-MAIRL方法的收是使用Lyapunov递归数学验证.
结论:
- 拟议的PI-MAIRL方法为了解复杂的无人机群行为提供了一个强大的框架.
- 这种方法促进了更好的群体智能,使得更有效的经验推断和模仿学习.
- 模拟研究表明了实际的好处,并强调了未来对无人机群控制和安全研究的挑战.
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