Alternating Mirror Descent for Constrained Min-Max GamesDownload PDF

Published: 31 Oct 2022, Last Modified: 12 Jan 2023NeurIPS 2022 AcceptReaders: Everyone
Keywords: Min-max games, regret bound, mirror descent, alternating method, Hamiltonian, symplectic integrator
TL;DR: We analyze the regret guarantees of the alternating mirror descent algorithm for constrained min-max games.
Abstract: In this paper we study two-player bilinear zero-sum games with constrained strategy spaces. An instance of natural occurrences of such constraints is when mixed strategies are used, which correspond to a probability simplex constraint. We propose and analyze the alternating mirror descent algorithm, in which each player takes turns to take action following the mirror descent algorithm for constrained optimization. We interpret alternating mirror descent as an alternating discretization of a skew-gradient flow in the dual space, and use tools from convex optimization and modified energy function to establish an $O(K^{-2/3})$ bound on its average regret after $K$ iterations. This quantitatively verifies the algorithm's better behavior than the simultaneous version of mirror descent algorithm, which is known to diverge and yields an $O(K^{-1/2})$ average regret bound. In the special case of an unconstrained setting, our results recover the behavior of alternating gradient descent algorithm for zero-sum games which was studied in (Bailey et al., COLT 2020).
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