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

Hugo Gimbert (1) , Youssouf Oualhadj (1) , Soumya Paul (2)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 IMSc - Institute of Mathematical Sciences [Chennai]

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.

Keywords: Quantitative analysis; Parity games; Markov decision processes

Domains: Computer Science and Game Theory [cs.GT]

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