Distributed Online Learning Algorithms for Aggregative Games Over Time-Varying Unbalanced Digraphs

被引：1
|
作者：
Zuo, Xiaolong [1 ]
Deng, Zhenhua [1 ]
机构：
[1] Cent South Univ, Sch Automat, Changsha 410075, Peoples R China
来源：
2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC | 2023年
关键词：
EQUILIBRIA;
D O I：
10.1109/CDC49753.2023.10383716
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, online aggregative games over time-varying unbalanced digraphs are studied, where the cost functions of players are time-varying and are gradually revealed to corresponding players only after decisions are made. Moreover, in the problems, players are subject to local convex set constraints and time-varying coupled nonlinear inequality constraints. To the best of our knowledge, no result about online games with unbalanced digraphs has been reported, let alone constrained online games. To solve the problem, a distributed online algorithm based on primal-dual, mirror descents and push-sum methods is developed. With the algorithm, sublinear dynamic regrets and constraint violations are established. Finally, online electricity market games illustrate the algorithm.
引用
收藏
页码：2278 / 2283
页数：6
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