Fast swap regret minimization and applications to approximate correlated equilibria
Binghui Peng, Aviad Rubinstein

TL;DR
This paper introduces a highly efficient algorithm for swap regret minimization that significantly reduces the number of rounds needed for convergence, leading to faster algorithms for approximate correlated equilibria in various game settings.
Contribution
It presents a simple, computationally efficient algorithm achieving near-optimal swap regret in polylogarithmic rounds, resolving key open problems in game theory and equilibrium computation.
Findings
Achieves $ ext{polylog}(n)$ rounds for $ ext{swap regret}$
Provides the first uncoupled dynamics converging to $ ext{approximate correlated equilibrium}$ in polylogarithmic rounds
Develops a $ ext{polylog}(n)$-bit communication protocol for $ ext{approximate correlated equilibrium}$
Abstract
We give a simple and computationally efficient algorithm that, for any constant , obtains -swap regret within only rounds; this is an exponential improvement compared to the super-linear number of rounds required by the state-of-the-art algorithm, and resolves the main open problem of [Blum and Mansour 2007]. Our algorithm has an exponential dependence on , but we prove a new, matching lower bound. Our algorithm for swap regret implies faster convergence to -Correlated Equilibrium (-CE) in several regimes: For normal form two-player games with actions, it implies the first uncoupled dynamics that converges to the set of -CE in polylogarithmic rounds; a -bit communication protocol for -CE in two-player games (resolving an open problem…
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Taxonomy
TopicsGame Theory and Applications · Bayesian Modeling and Causal Inference · Advanced Bandit Algorithms Research
MethodsSparse Evolutionary Training
