分布式学习在线随机混合平衡问题与重尾噪音
IEEE transactions on neural networks and learning systems
|February 9, 2026
概括
本研究介绍了一种在线分布式学习算法,用于在不确定,动态环境中使用多代理系统 (MAS) 的混合平衡问题 (MEP). 该算法在沉重的尾巴噪音下实现了亚线性动态遗憾,在复杂的设置中证明了有效性.
科学领域:
- 优化理论 优化理论
- 分布式系统 分布式系统
- 机器学习 机器学习
背景情况:
- 在不确定和动态环境中研究混合平衡问题 (MEP).
- 使用多代理系统 (MAS),其中代理人具有有限的本地信息,并通过时间变化的图形进行通信.
- 解决了随机,时间变化的双功能的挑战,只有在决策后才发现.
研究的目的:
- 开发一种在线分布式学习算法,用于在复杂环境中解决欧洲议会议员的问题.
- 用动态遗憾来分析算法的性能.
- 建立遗憾的理论界限,特别是在沉重的尾巴噪音条件下.
主要方法:
- 采用一个多代理系统 (MAS),具有分散的决策.
- 提出了一个在线分布式学习算法,集成镜像下降和剪切策略.
- 分析与离线基准对比的动态遗憾,建立高概率的边界.
主要成果:
- 拟议的算法实现了在重尾噪声下高概率的亚线性动态遗憾.
- 性能在理论上得到验证,如果溶液序列变化是有限的,则显示遗憾会以次线性增长.
- 一个模拟示例证实了理论发现.
结论:
- 开发的算法对于在不确定的和动态的多代理环境中解决混合平衡问题是有效的.
- 理论分析为性能提供了强有力的保证,即使数据杂且不断变化.
- 这些发现有助于理解在具有挑战性的环境中分布式优化.
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