针对大型多代理系统的分布式自适应集群控制
IEEE transactions on neural networks and learning systems
|February 15, 2024
概括
本研究介绍了一种用于大型多代理系统 (LS-MAS) 的新型分布式集群控制方法,使用混合游戏理论和强化学习来克服通信复杂性并改善不确定环境中的协调.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 多代理系统 多代理系统
- 游戏理论 游戏理论
背景情况:
- 现有的集群方法在大型多代理系统 (LS-MAS) 中与通信复杂性和维度的诅咒作斗争.
- 平均场游戏 (MFG) 使用概率密度函数简化了交互,但缺乏强大的协调机制以实现高效的集群.
研究的目的:
- 为LS-MAS在不确定的环境中开发一种新的分布式集群控制方法.
- 通过解决沟通复杂性和协调挑战来提高群聚的性能.
主要方法:
- 将LS-MAS分解为领导者-追随者子组.
- 混合游戏理论将合作,斯塔克尔伯格和MFG结合起来,用于集团间和集团内部的互动.
- 基于分布自适应控制的层次演员-关键质量强化学习.
主要成果:
- 成功开发了一种用于LS-MAS的新型分布式集群控制方法.
- 混合游戏结构和强化学习使得适应性和高效的群.
- 数字模拟和利亚普诺夫分析证实了该方法的有效性.
结论:
- 拟议的方法有效地克服了LS-MASs现有的集群技术的局限性.
- 混合游戏理论和强化学习方法为分布式适应群组提供了强大的解决方案.
- 这项工作推进了复杂环境中大规模自主系统的控制策略.
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