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Updated: Jan 16, 2026

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The HoneyComb Paradigm for Research on Collective Human Behavior
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分布式过网强化学习用于在异构的多层多代理系统中实现输出共识
IEEE transactions on neural networks and learning systems
|September 29, 2025
概括
本研究解决了使用新的FilterNet强化学习 (RL) 架构的多代理系统中的领导者-追随者共识问题. 过网框架有效地达成共识,优于现有方法.
科学领域:
- 控制理论 控制理论
- 多代理系统 多代理系统
- 游戏理论 游戏理论
背景情况:
- 在复杂的多代理系统中研究领导者-追随者共识,由于异质动态和内部代理目标,提出了挑战.
- 现有的方法往往在这种系统的去中心化控制和数据管理方面扎.
研究的目的:
- 开发一个分布式控制框架,以在具有异质动态和多个内部参与者的多代理系统中实现输出共识.
- 设计一个强化学习 (RL) 架构,使得有效的,分散的控制,没有大量的数据存储.
主要方法:
- 制定了这个问题作为每个代理的多人差分游戏,旨在控制纳什平衡.
- 引入了一个分布式控制框架,集成了前 (调节器) 和反 (游戏理论里卡蒂) 组件.
- 开发了一个四层的FilterNet RL架构,用于政策识别,初始化,异步更新和实时控制.
主要成果:
- 过网架构有效地解决了控制解决方案,减少了数据需求并加速了融合.
- 理论上的保证证实了拟议方法的可靠性和趋同性.
- 数字模拟表明了FilterNet方法的有效性和优越性,与现有的方法相比.
结论:
- 拟议的FilterNet RL框架为复杂的多代理系统中的领导者-追随者共识问题提供了强大而高效的解决方案.
- 这种方法通过有效地整合游戏理论和强化学习来推进分散的控制策略.
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