On zero-sum two-person undiscounted semi-Markov games with a multichain structure

Author:

Mondal Prasenjit

Abstract

Abstract Zero-sum two-person finite undiscounted (limiting ratio average) semi-Markov games (SMGs) are considered with a general multichain structure. We derive the strategy evaluation equations for stationary strategies of the players. A relation between the payoff in the multichain SMG and that in the associated stochastic game (SG) obtained by a data-transformation is established. We prove that the multichain optimality equations (OEs) for an SMG have a solution if and only if the associated SG has optimal stationary strategies. Though the solution of the OEs may not be optimal for an SMG, we establish the significance of studying the OEs for a multichain SMG. We provide a nice example of SMGs in which one player has no optimal strategy in the stationary class but has an optimal semistationary strategy (that depends only on the initial and current state of the game). For an SMG with absorbing states, we prove that solutions in the game where all players are restricted to semistationary strategies are solutions for the unrestricted game. Finally, we prove the existence of stationary optimal strategies for unichain SMGs and conclude that the unichain condition is equivalent to require that the game satisfies some recurrence/ergodicity/weakly communicating conditions.

Publisher

Cambridge University Press (CUP)

Subject

Applied Mathematics,Statistics and Probability

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Zero-sum infinite-horizon discounted piecewise deterministic Markov games;Mathematical Methods of Operations Research;2023-02-08

2. On Zero-Sum Two Person Perfect Information Semi-Markov Games;Lecture Notes in Networks and Systems;2023

3. Semi-stationary Equilibrium Strategies in Non-cooperative N-person Semi-Markov Games;Mathematical Analysis and Applications in Modeling;2020

4. Computing semi-stationary optimal policies for multichain semi-Markov decision processes;Annals of Operations Research;2017-10-14

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