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Title: Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
We introduce a new approach for computing optimal equilibria and mechanisms via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that optimal equilibria are minimax equilibrium strategies of a player in an extensiveform zero-sum game. This reformulation allows us to apply techniques for learning in zero-sum games, yielding the first learning dynamics that converge to optimal equilibria, not only in empirical averages, but also in iterates. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning.  more » « less
Award ID(s):
1901403
PAR ID:
10550008
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Publisher / Repository:
NeurIPS23
Date Published:
Format(s):
Medium: X
Location:
New Orleans, LA
Sponsoring Org:
National Science Foundation
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