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 : https://apps.dtic.mil/dtic/tr/fulltext/u2/1017108.pdf


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
      Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE