@article {Burnetas200353,
	title = {Asymptotic bayes analysis for the finite-horizon one-armed-bandit problem},
	journal = {Probability in the Engineering and Informational Sciences},
	volume = {17},
	number = {1},
	year = {2003},
	note = {cited By 3},
	pages = {53-82},
	abstract = {The multiarmed-bandit problem is often taken as a basic model for the trade-off between the exploration and utilization required for efficient optimization under uncertainty. In this article, we study the situation in which the unknown performance of a new bandit is to be evaluated and compared with that of a known one over a finite horizon. We assume that the bandits represent random variables with distributions from the one-parameter exponential family. When the objective is to maximize the Bayes expected sum of outcomes over a finite horizon, it is shown that optimal policies tend to simple limits when the length of the horizon is large.},
	issn = {02699648},
	doi = {10.1017/S0269964803171045},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0037249547\&doi=10.1017\%2fS0269964803171045\&partnerID=40\&md5=7fda41611365aff4704ce2e85c375c59},
	author = {Burnetas, AN and Katehakis, M.N.}
}
