Algorithms for zero-sum stochastic games with the risk-sensitive average criterion

Abstract

This paper is an attempt to compute the value and saddle points of zero-sum risk-sensitive average stochastic games. For the average games with finite states and actions, we first introduce the so-called irreducibility coefficient and then establish its equivalence to the irreducibility condition. Using this equivalence,we develop an iteration algorithm to compute $\epsilon$-approximations of the value (for any given $\epsilon >0$) and show its convergence. Based on $\epsilon$-approximations of the value and the irreducibility coefficient, we further propose another iteration algorithm, which is proved to obtain $\epsilon$-saddle points in finite steps. Finally, a numerical example of energy management in smart grids is provided to illustrate our results.