@article {Burnetas20161,
	title = {ASYMPTOTICALLY OPTIMAL MULTI-ARMED BANDIT POLICIES UNDER A COST CONSTRAINT},
	journal = {Probability in the Engineering and Informational Sciences},
	year = {2016},
	note = {cited By 0; Article in Press},
	pages = {1-27},
	publisher = {Cambridge University Press},
	abstract = {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 {\textcopyright} Cambridge University Press 2016},
	issn = {02699648},
	doi = {10.1017/S026996481600036X},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990210313\&doi=10.1017\%2fS026996481600036X\&partnerID=40\&md5=397c031f3dffd758c497202a49b2a4cc},
	author = {Burnetas, A and Kanavetas, O. and Katehakis, M.N.}
}
