Preprints, Working Papers, ... Year : 2011

Computing Optimal Strategies for Markov Decision Processes with Parity and Positive-Average Conditions

Abstract

We study Markov decision processes (one-player stochastic games) equipped with parity and positive-average conditions. In these games, the goal of the player is to maximize the probability that both the parity and the positive-average conditions are fulfilled. We show that the values of these games are computable. We also show that optimal strategies exist, require only finite memory and can be effectively computed.

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Dates and versions

hal-00559173 , version 1 (25-01-2011)
hal-00559173 , version 2 (02-02-2011)
hal-00559173 , version 3 (14-04-2011)

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  • HAL Id : hal-00559173 , version 3

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Hugo Gimbert, Youssouf Oualhadj, Soumya Paul. Computing Optimal Strategies for Markov Decision Processes with Parity and Positive-Average Conditions. 2011. ⟨hal-00559173v3⟩
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