A convex programming-based algorithm for mean payoff stochastic games with perfect information

E Boros, K Elbassioni, V Gurvich, K Makino - Optimization Letters, 2017 - Springer
E Boros, K Elbassioni, V Gurvich, K Makino
Optimization Letters, 2017Springer
We consider two-person zero-sum stochastic mean payoff games with perfect information, or
BWR-games, given by a digraph G=(V, E), with local rewards r: E→ Z, and three types of
positions: black VB, white VW, and random VR forming a partition of V. It is a long-standing
open question whether a polynomial time algorithm for BWR-games exists, even when| VR|=
0. In fact, a pseudo-polynomial algorithm for BWR-games would already imply their
polynomial solvability. In this short note, we show that BWR-games can be solved via …
Abstract
We consider two-person zero-sum stochastic mean payoff games with perfect information, or BWR-games, given by a digraph , with local rewards , and three types of positions: black , white , and random forming a partition of V. It is a long-standing open question whether a polynomial time algorithm for BWR-games exists, even when . In fact, a pseudo-polynomial algorithm for BWR-games would already imply their polynomial solvability. In this short note, we show that BWR-games can be solved via convex programming in pseudo-polynomial time if the number of random positions is a constant.
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