@article {Burnetas1997222,
	title = {Optimal adaptive policies for markov decision processes},
	journal = {Mathematics of Operations Research},
	volume = {22},
	number = {1},
	year = {1997},
	note = {cited By 42},
	pages = {222-255},
	abstract = {In this paper we consider the problem of adaptive control for Markov Decision Processes. We give the explicit form for a class of adaptive policies that possess optimal increase rate properties for the total expected finite horizon reward, under sufficient assumptions of finite state-action spaces and irreducibility of the transition law. A main feature of the proposed policies is that the choice of actions, at each state and time period, is based on indices that are inflations of the right-hand side of the estimated average reward optimality equations.},
	keywords = {Adaptive control systems, Decision theory, Dynamic programming, Estimation, Markov processes, Markovian decision process (MDP), Multi armed bandit (MAB) problem, Optimal control systems, Process control, State space methods, Statistics},
	issn = {0364765X},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031070051\&partnerID=40\&md5=a3d110a158d5a27a6b74fd9d054a049b},
	author = {Burnetas, AN and Katehakis, M.N.}
}
