Institute of Information Theory and Automation

Publication details

Identification of Optimal Policies in Markov Decision Processes

Journal Article

Sladký Karel

serial: Kybernetika, 3 (2010), p. 558-570

action: International Conference on Mathematical Methods in Economy and Industry, (České Budějovice, CZ, 15.06.2009-18.06.2009)

research: CEZ:AV0Z10750506

project(s): GA402/08/0107, GA ČR, GA402/07/1113, GA ČR

keywords: finite state Markov decision processes, discounted and average costs, elimination of suboptimal policies

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abstract (eng):

In this note we focus attention on identifying optimal policies and on elimination suboptimal policies minimizing optimality criteria in discrete-time Markov decision processes with finite state space and compact action set. We present unified approach to value iteration algorithms that enables to generate lower and upper bounds on optimal values, as well as on the current policy. Using the modified value iterations it is possible to eliminate suboptimal actions and to identify an optimal policy or nearly optimal policies in a finite number of steps without knowing precise values of the performance function.


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Last modification: 21.12.2012
Institute of Information Theory and Automation