Displaying similar documents to “Policy iteration for continuous-time average reward Markov decision processes in Polish spaces.”

A stopping rule for discounted Markov decision processes with finite action sets

Raúl Montes-de-Oca, Enrique Lemus-Rodríguez, Daniel Cruz-Suárez (2009)

Kybernetika

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In a Discounted Markov Decision Process (DMDP) with finite action sets the Value Iteration Algorithm, under suitable conditions, leads to an optimal policy in a finite number of steps. Determining an upper bound on the necessary number of steps till gaining convergence is an issue of great theoretical and practical interest as it would provide a computationally feasible stopping rule for value iteration as an algorithm for finding an optimal policy. In this paper we find such a bound...

Identification of optimal policies in Markov decision processes

Karel Sladký (2010)

Kybernetika

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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...