Contraction Conditions for Average and α-Discount Optimality in Countable State Markov Games with Unbounded Rewards

Abstract

The goal of this paper is to provide a theory of N-person Markov games with unbounded cost, for a countable state space and compact action spaces. We investigate both the finite and infinite horizon problems. For the latter, we consider the discounted cost as well as the expected average cost. We present conditions for the infinite horizon problems for which equilibrium policies exist for all players within the stationary policies, and show that the costs in equilibrium policies exist for all players within the stationary policies, and show that the costs in equilibrium satisfy the optimality equations. Similar results are obtained for the finite horizon costs, for which equilibrium policies are shown to exist for all players within the Markov policies. As special case of N-person games, we investigate the zero-sum (2 players) game, for which we establish the convergence of the value iteration algorithm. We conclude by studying an application of a zero-sum Markov game in a queueing model.