Best response dynamics in partially observed zero-sum linear quadratic games converge numerically after a few iterations, and low-order belief feedback strategies approximate the Nash equilibrium.
Computing Best-Response Strategies in Infinite Games of Incomplete Information
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abstract
We describe an algorithm for computing best response strategies in a class of two-player infinite games of incomplete information, defined by payoffs piecewise linear in agents' types and actions, conditional on linear comparisons of agents' actions. We show that this class includes many well-known games including a variety of auctions and a novel allocation game. In some cases, the best-response algorithm can be iterated to compute Bayes-Nash equilibria. We demonstrate the efficiency of our approach on existing and new games.
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Best Response Convergence for Zero-sum Stochastic Dynamic Games with Partial and Asymmetric Information
Best response dynamics in partially observed zero-sum linear quadratic games converge numerically after a few iterations, and low-order belief feedback strategies approximate the Nash equilibrium.