On Canonical Forms for Zero-Sum Stochastic Mean Payoff Games

Endre Boros, Khaled Elbassioni, Vladimir Gurvich, Kazuhisa Makino

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We consider two-person zero-sum mean payoff undiscounted stochastic games and obtain sufficient conditions for the existence of a saddle point in uniformly optimal stationary strategies. Namely, these conditions enable us to bring the game, by applying potential transformations, to a canonical form in which locally optimal strategies are globally optimal, and hence the value for every initial position and the optimal strategies of both players can be obtained by playing the local game at each state. We show that these conditions hold for the class of additive transition (AT) games, that is, the special case when the transitions at each state can be decomposed into two parts, each controlled completely by one of the two players. An important special case of AT-games form the so-called BWR-games which are played by two players on a directed graph with positions of three types: Black, White and Random. We give an independent proof for the existence of a canonical form in such games, and use this result to derive the existence of a canonical form (and hence, of a saddle point in uniformly optimal stationary strategies) in a wide class of games, which includes stochastic games with perfect information (PI), switching controller (SC) games and additive rewards, additive transition (ARAT) games. Unlike the proof for AT-games, our proof for the BWR-case does not rely on the existence of a saddle point in stationary strategies. We also derive some algorithmic consequences from these our reductions to BWR-games, in terms of solving PI-, and ARAT-games in sub-exponential time.

Original languageBritish English
Pages (from-to)128-161
Number of pages34
JournalDynamic Games and Applications
Volume3
Issue number2
DOIs
StatePublished - Jun 2013

Keywords

  • Equilibrium
  • Mean-payoff games
  • Saddle point
  • Stochastic games
  • Zero-sum

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