Algorithms for discounted stochastic games

In this paper, a two-person zero-sum discounted stochastic game with a finite state space is considered. The movement of the game from state to state is jointly controlled by the two players with a finite number of alternatives available to each player in each of the states. We present two convergent algorithms for arriving at minimax strategies for the players and the value of the game. The two algorithms are compared with respect to computational efficiency. Finally, a possible extension to nonzero sum stochastic game is suggested.This research was supported in part by funds allocated to the Department of Operations Research, School of Management, Case Western Reserve University under Contract No. DAHC 19-68-C-0007 (Project Themis) with the U.S. Army Research Office, Durham, North Carolina. The authors thank the referees for their valuable suggestions.