Accession Number : AD1017108

Title :   Stochastic Online Learning in Dynamic Networks under Unknown Models

Descriptive Note : Technical Report,29 Jun 2012,28 Jun 2016

Corporate Author : University of California - Davis Davis United States

Personal Author(s) : Zhao,Qing

Full Text :

Report Date : 02 Aug 2016

Pagination or Media Count : 15

Abstract : This research aims to develop fundamental theories and practical algorithms for distributed, robust, and real-time learning in dynamic tactical networks. The overall objective is to significantly move the frontiers of knowledge in stochastic learning in the classic multi-armed bandit by systematically relaxing traditionally adopted restrictive assumptions.

Descriptors :   network topology , information exchange , algorithms , GAME THEORY , stochastic processes

Subject Categories : Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE