Learning Correlated Equilibria in Extensive Form Games

Abstract

We formalize an efficient class of counterfactual regret minimization algorithms exploiting the “sequence form” to compute “$\Phi$-equilibria” – a generalized class of equilibrium concepts defined within general-sum extensive form games of imperfect information. We develop increasingly strong notions of no-regret, mapping those notions directly onto concepts of interest, such as agent- and extensive-form correlated and coarse correlated equilibria.

Publication
Working Paper

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Reca Sarfati
Senior Research Analyst

I am a Senior Research Analyst on the dynamic stochastic general equilibrium (DSGE) team in the Macroeconomic and Monetary Studies function at the NY Fed. Views expressed are my own.