Small Nash Equilibrium Certificates in Very Large Games
Brian Hu Zhang, Tuomas Sandholm
Abstract
In many game settings, the game is not explicitly given but is only accessible by playing it. While there have been impressive demonstrations in such settings, prior techniques have not offered safety guarantees, that is, guarantees on the game-theoretic exploitability of the computed strategies. In this paper we introduce an approach that shows that it is possible to provide exploitability guarantees in such settings without ever exploring the entire game. We introduce a notion of a certificatae of an extensive-form approximate Nash equilibrium. For verifying a certificate, we give an algorithm that runs in time linear in the size of the certificate rather than the size of the whole game. In zero-sum games, we further show that an optimal certificate---given the exploration so far---can be computed with any standard game-solving algorithm (e.g., using a linear program or counterfactual regret minimization). However, unlike in the cases of normal form or perfect information, we show that certain families of extensive-form games do not have small approximate certificates, even after making extremely nice assumptions on the structure of the game. Despite this difficulty, we find experimentally that very small certificates, even exact ones, often exist in large and even in infinite games. Overall, our approach enables one to try one's favorite exploration strategies while offering exploitability guarantees, thereby decoupling the exploration strategy from the equilibrium-finding process.
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Install the CLIlune papers fulltext e5a59de2-c937-44e1-843f-ba545c6a0eb7Cited by top-tier papers3
- Subgame solving without common knowledgeBrian Hu Zhang, Tuomas SandholmNeurIPS 2021 · 21 citations
- Finding and Certifying (Near-)Optimal Strategies in Black-Box Extensive-Form GamesBrian Hu Zhang, Tuomas SandholmAAAI 2021 · 15 citations
- Computing Optimal Nash Equilibria in Multiplayer GamesYouzhi Zhang, Bo An, Venkatramanan Siva SubrahmanianNeurIPS 2023 · 7 citations
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