@conference {Burnetas19951402,
	title = {Efficient estimation and control for Markov processes},
	booktitle = {Proceedings of the IEEE Conference on Decision and Control},
	volume = {2},
	year = {1995},
	note = {cited By 2; Conference of Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) ; Conference Date: 13 December 1995 Through 15 December 1995; Conference Code:44367},
	pages = {1402-1407},
	publisher = {IEEE, Piscataway, NJ, United States},
	organization = {IEEE, Piscataway, NJ, United States},
	address = {New Orleans, LA, USA},
	abstract = {We consider the problem of sequential control for a finite state and action Markovian Decision Process with incomplete information regarding the transition probabilities P ∈ Papprox. Under suitable irreducibility assumptions for Papprox.. We construct adaptive policies that maximize the rate of convergence of realized rewards to that of the optimal (non adaptive) policy under complete information. These adaptive policies are specified via an easily computable index function, of states, controls and statistics, so that one takes a control with the largest index value in the current state in every period.},
	keywords = {Adaptive control systems, Constraint theory, Control theory, Convergence of numerical methods, Decision theory, Index function, Markov decision process, Markov processes, Mathematical models, probability, Queueing control, Queueing theory, State estimation, Statistical methods, Transition probabilities},
	issn = {01912216},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029517410\&partnerID=40\&md5=690fdefb70e718ba7a488ebe838d6ddf},
	author = {Burnetas, Apostolos N and Katehakis, Michael N}
}
