Online Bandit Convex Optimization Over A Network

被引：0

作者：

Yuan, Deming ^{[1
]}

Ho, Daniel W. C. ^{[2
]}

Hong, Yiguang ^{[3
]}

Jiang, Guoping ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210023, Peoples R China

[2] City Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China

[3] Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China

来源：

PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 | 2016年

基金：

中国国家自然科学基金;

关键词：

Online Convex Optimization; Distributed Optimization; Bandit; Regret; MULTIAGENT OPTIMIZATION; CONSENSUS; ALGORITHM;

D O I：

暂无

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 we study the online bandit optimization over an undirected and connected network. In this setting, a network consists of multiple interacting agents plays a repeated game of T rounds with the dynamic environment. The objective function is a sum of all the agents' local cost functions that change over time, due to the uncertainties in the environment; moreover, each agent can only observe the value of each cost function at a single point at each round. We propose an efficient algorithm for distributed online bandit optimization with strongly convex cost functions and obtain a bound of (O) over tilde (T-2/3) on the expected regret.

引用

页码：8090 / 8095

页数：6

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