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arXiv · 2003.11727

Last Round Convergence and No-Instant Regret in Repeated Games with Asymmetric Information

Le Cong Dinh·Long Tran-Thanh·Tri-Dung Nguyen·Alain B. Zemkoho
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Abstract

This paper considers repeated games in which one player has more information about the game than the other players. In particular, we investigate repeated two-player zero-sum games where only the column player knows the payoff matrix A of the game. Suppose that while repeatedly playing this game, the row player chooses her strategy at each round by using a no-regret algorithm to minimize her (pseudo) regret. We develop a no-instant-regret algorithm for the column player to exhibit last round convergence to a minimax equilibrium. We show that our algorithm is efficient against a large set of popular no-regret algorithms of the row player, including the multiplicative weight update algorithm, the online mirror descent method/follow-the-regularized-leader, the linear multiplicative weight update algorithm, and the optimistic multiplicative weight update.

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Le Cong Dinh, Long Tran-Thanh, Tri-Dung Nguyen, Alain B. Zemkoho. 2020-03-26. Last Round Convergence and No-Instant Regret in Repeated Games with Asymmetric Information. https://arxiv.org/abs/2003.11727

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