Fast computation of Nash Equilibria in Imperfect Information Games

Jul 12, 2020

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We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential imperfect information form. These algorithms update the policy of each player with a mirror-descent step to minimize the loss of playing against an improved opponent. We establish a convergence result to the set of Nash equilibria where the speed of convergence depends on the amount of improvement of the opponent policies. In addition, if the improved opponent is a best response, then an exponential convergence rate is achieved.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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