<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Burnetas, A</style></author><author><style face="normal" font="default" size="100%">Kanavetas, O.</style></author><author><style face="normal" font="default" size="100%">Katehakis, M.N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ASYMPTOTICALLY OPTIMAL MULTI-ARMED BANDIT POLICIES UNDER A COST CONSTRAINT</style></title><secondary-title><style face="normal" font="default" size="100%">Probability in the Engineering and Informational Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990210313&amp;doi=10.1017%2fS026996481600036X&amp;partnerID=40&amp;md5=397c031f3dffd758c497202a49b2a4cc</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Cambridge University Press</style></publisher><pages><style face="normal" font="default" size="100%">1-27</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the multi-armed bandit problem under a cost constraint. Successive samples from each population are i.i.d. with unknown distribution and each sample incurs a known population-dependent cost. The objective is to design an adaptive sampling policy to maximize the expected sum of n samples such that the average cost does not exceed a given bound sample-path wise. We establish an asymptotic lower bound for the regret of feasible uniformly fast convergent policies, and construct a class of policies, which achieve the bound. We also provide their explicit form under Normal distributions with unknown means and known variances. Copyright © Cambridge University Press 2016</style></abstract><notes><style face="normal" font="default" size="100%">cited By 0; Article in Press</style></notes></record></records></xml>