5 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…
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…
LiteEFG: An Efficient Python Library for Solving Extensive-form Games
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
LiteEFG is an efficient library with easy-to-use Python bindings, which can solve multiplayer extensive-form games (EFGs). LiteEFG enables the user to express computation graphs in…