7 papers · 1 filter
Equilibrium with Internal Transfers
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
Nash equilibrium (NE) arises from selfish utility maximization, yet its social welfare can be arbitrarily far from optimal. Moreover, computing an NE is intractable in general. We…
Computing Equilibrium beyond Unilateral Deviation
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviating unilaterally. They offer no…
Differentially Private Equilibrium Finding in Polymatrix Games
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
We study equilibrium finding in polymatrix games under differential privacy constraints. Prior work in this area fails to achieve both high-accuracy equilibria and a low privacy bu…
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions
Chanwoo Park, Kaiqing Zhang, Asuman Ozdaglar
We study a new class of Markov games, \emph(multi-player) zero-sum Markov Games} with \emph{Networked separable interactions} (zero-sum NMGs), to model the local interaction struct…
A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
Policy gradient methods have become a staple of any single-agent reinforcement learning toolbox, due to their combination of desirable properties: iterate convergence, efficient us…
The Power of Regularization in Solving Extensive-Form Games
Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu +1
In this paper, we investigate the power of {\it regularization}, a common technique in reinforcement learning and optimization, in solving extensive-form games (EFGs). We propose a…