不完整信息动态斯塔克尔伯格平衡通过分布式分布强大的反方法寻求
IEEE transactions on cybernetics
|October 7, 2025
概括
在Stackelberg游戏中的领导者现在可以使用一种新的分布式算法优化未知的追随者目标和随机干扰的策略. 这种数据驱动的方法可以在不确定性下加强决策,而不需要系统模型.
科学领域:
- 控制理论 控制理论
- 游戏理论 游戏理论
- 优化优化 优化优化
背景情况:
- 传统的斯塔克尔伯格游戏假设领导者拥有关于追随者的目标的完整信息.
- 领导者经常面临未知的追随者行为和随机系统干扰的挑战.
- 现有的方法在追随者响应中与分布不确定性作斗争.
研究的目的:
- 为具有不完整信息的多人Stackelberg游戏开发一个强大的框架.
- 为了解决追随客观函数中的分布不确定性.
- 为了使领导者能够有效地操纵物理植物状态,尽管未知的追随者策略.
主要方法:
- 重构游戏作为一个分布稳定的寻求平衡问题的问题.
- 开发一个完全分布式的联合学习 (FL) 算法.
- 通过邻居通信和本地梯度更新进行数据驱动的状态估计.
主要成果:
- 在非凸的设置中平衡存在的特征.
- 建立通信/梯度错误和系统能量功能的关系.
- 对拟议的算法后悔的上限进行严格的分析.
结论:
- 拟议的FL算法有效地实现了针对不确定的随机扰动的分布稳定解决方案.
- 数据驱动的方法在没有对系统模型或干扰分布的预先了解的情况下运行.
- 通过案例研究证明了有效性,突出了实际适用性.
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