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Michal Šustr

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3 papers

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AAAI Conference 2024 Conference Paper

Learning Not to Regret

David Sychrovský
Michal Šustr
Elnaz Davoodi
Michael Bowling
Marc Lanctot
Martin Schmid

The literature on game-theoretic equilibrium finding predominantly focuses on single games or their repeated play. Nevertheless, numerous real-world scenarios feature playing a game sampled from a distribution of similar, but not identical games, such as playing poker with different public cards or trading correlated assets on the stock market. As these similar games feature similar equilibra, we investigate a way to accelerate equilibrium finding on such a distribution. We present a novel ``learning not to regret'' framework, enabling us to meta-learn a regret minimizer tailored to a specific distribution. Our key contribution, Neural Predictive Regret Matching, is uniquely meta-learned to converge rapidly for the chosen distribution of games, while having regret minimization guarantees on any game. We validated our algorithms' faster convergence on a distribution of river poker games. Our experiments show that the meta-learned algorithms outpace their non-meta-learned counterparts, achieving more than tenfold improvements.

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AAMAS Conference 2021 Conference Paper

Sound Algorithms in Imperfect Information Games

Michal Šustr
Martin Schmid
Matej Moravćík
Neil Burch
Marc Lanctot
Michael Bowling

Search has played a fundamental role in computer game research since the very beginning. And while online search has been commonly used in perfect information games such as Chess and Go, online search methods for imperfect information games have only been introduced relatively recently. This paper addresses the question of what is a sound online algorithm in an imperfect information setting of two-player zero-sum games? We argue that the fixedstrategy definitions of exploitability and epsilon-Nash equilibria are ill suited to measure the worst-case performance of an online algorithm. We thus formalize epsilon-soundness, a concept that connects the worst-case performance of an online algorithm to the performance of an epsilon-Nash equilibrium. Our definition of soundness and the consistency hierarchy finally provide appropriate tools to analyze online algorithms in repeated imperfect information games. We thus inspect some of the previous online algorithms in a new light, bringing new insights into their worst case performance guarantees.

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AAMAS Conference 2019 Conference Paper

Monte Carlo Continual Resolving for Online Strategy Computation in Imperfect Information Games

Michal Šustr
Vojtech Kovarik
Viliam Lisý

Online game playing algorithms produce high-quality strategies with a fraction of memory and computation required by their offline alternatives. Continual Resolving (CR) is a recent theoretically sound approach to online game playing that has been used to outperform human professionals in poker. However, parts of the algorithm were specific to poker, which enjoys many properties not shared by other imperfect information games. We present a domain-independent formulation of CR applicable to any twoplayer zero-sum extensive-form games (EFGs). It works with an abstract resolving algorithm, which can be instantiated by various EFG solvers. We further describe and implement its Monte Carlo variant (MCCR) which uses Monte Carlo Counterfactual Regret Minimization (MCCFR) as a resolver. We prove the correctness of CR and show an O(T−1/2)-dependence of MCCR’s exploitability on the computation time. Furthermore, we present an empirical comparison of MCCR with incremental tree building to Online Outcome Sampling and Information-set MCTS on several domains.

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