A Pumping Algorithm for Ergodic Stochastic Mean Payoff Games with Perfect Information

Abstract. We consider two-person zero-sum stochastic mean payoff games with perfect information, or BWR-games, given by a digraph G = (V = VB VW VR, E), with local rewards r : E R, and three types of vertices: black VB, white VW , and random VR. The game is played by two players, White and Black: When the play is at a white (black) vertex v, White (Black) selects an outgoing arc (v, u). When the play is at a random vertex v, a vertex u is picked with the given probability p(v, u). In all cases, Black pays White the value r(v, u). The play continues forever, and White aims to maximize (Black aims to minimize) the limiting mean (that is, average) payoff. It was recently shown in [BEGM09a] that BWR-games are polynomially equivalent with the classical Gillette games, which include many well-known subclasses, such as cyclic games, simple stochastic games (SSG), stochastic parity games, and Markov decision processes. In this paper, we give a new algorithm for solving BWR-games in the ergodi...

• Type: Conference
• Year: 2010
• Where: IPCO
• Authors: Endre Boros, Khaled M. Elbassioni, Vladimir Gurvich, Kazuhisa Makino
• Keywords: IPCO 2010, Optimization, Stochastic Mean Payoff, VB VW VR, Vertex V