Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond
Gabriele Farina, Tuomas Sandholm
Abstract
Unlike normal-form games, where correlated equilibria have been studied for more than 45 years, extensive-form correlation is still generally not well understood. Part of the reason for this gap is that the sequential nature of extensive-form games allows for a richness of behaviors and incentives that are not possible in normal-form settings. This richness translates to a significantly different complexity landscape surrounding extensive-form correlated equilibria. As of today, it is known that finding an optimal extensive-form correlated equilibrium (EFCE), extensive-form coarse correlated equilibrium (EFCCE), or normal-form coarse correlated equilibrium (NFCCE) in a two-player extensive-form game is computationally tractable when the game does not include chance moves, and intractable when the game involves chance moves. In this paper we significantly refine this complexity threshold by showing that, in two-player games, an optimal correlated equilibrium can be computed in polynomial time, provided that a certain condition is satisfied. We show that the condition holds, for example, when all chance moves are public, that is, both players observe all chance moves. This implies that an optimal EFCE, EFCCE and NFCCE can be computed in polynomial time in the game size in two-player games with public chance moves, providing the biggest positive complexity result surrounding extensive-form correlation in more than a decade.
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Cited by top-tier papers9
- No-Regret Learning Dynamics for Extensive-Form Correlated EquilibriumAndrea Celli, Alberto Marchesi, Gabriele Farina, Nicola GattiNeurIPS 2020 · 48 citations
- Connecting Optimal Ex-Ante Collusion in Teams to Extensive-Form Correlation: Faster Algorithms and Positive Complexity ResultsGabriele Farina, Andrea Celli, Nicola Gatti, Tuomas SandholmICML 2021 · 29 citations
- Team Correlated Equilibria in Zero-Sum Extensive-Form Games via Tree DecompositionsBrian Hu Zhang, Tuomas SandholmAAAI 2022 · 26 citations
- Team-PSRO for Learning Approximate TMECor in Large Team Games via Cooperative Reinforcement LearningStephen McAleer, Gabriele Farina, Gaoyue Zhou, Mingzhi Wang et al.NeurIPS 2023 · 18 citations
- Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form GamesBrian Hu Zhang, Gabriele Farina, Ioannis Anagnostides, Federico Cacciamani et al.NeurIPS 2023 · 17 citations
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