9 papers
Constant Individual Regret in General Games
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual regret retain a polylogarithmic dependence on the horizon. We remove…
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…
Online Learning and Equilibrium Computation with Ranking Feedback
Mingyang Liu, Yongshan Chen, Zhiyuan Fan +3
Online learning in arbitrary, and possibly adversarial, environments has been extensively studied in sequential decision-making, and it is closely connected to equilibrium computat…
UFT: Unifying Supervised and Reinforcement Fine-Tuning
Mingyang Liu, Gabriele Farina, Asuman Ozdaglar
Post-training has demonstrated its importance in enhancing the reasoning capabilities of large language models (LLMs). The primary post-training methods can be categorized into sup…
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…
Reevaluating Policy Gradient Methods for Imperfect-Information Games
Max Rudolph, Nathan Lichtle, Sobhan Mohammadpour +6
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed nu…
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…