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20182026
most citedDynamic Programming Principles for Mean-Field Controls with Learning

16 citations · 101 across the 22 of their papers we have counts for

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8 papers · 1 filter

math.OC2023★ 2 cited

Fast Policy Learning for Linear Quadratic Control with Entropy Regularization

Xin Guo, Xinyu Li, Renyuan Xu

This paper proposes and analyzes two new policy learning methods: regularized policy gradient (RPG) and iterative policy optimization (IPO), for a class of discounted linear-quadra…

math.OC2023

Linear-quadratic Gaussian Games with Asymmetric Information: Belief Corrections Using the Opponents Actions

Ben Hambly, Renyuan Xu, Huining Yang

We consider two-player non-zero-sum linear-quadratic Gaussian games in which both players aim to minimize a quadratic cost function while controlling a linear and stochastic state…

math.OC2021★ 1 cited

Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games

Ben Hambly, Renyuan Xu, Huining Yang

We consider a general-sum N-player linear-quadratic game with stochastic dynamics over a finite horizon and prove the global convergence of the natural policy gradient method to th…

math.OC2020★ 12 cited

Entropy Regularization for Mean Field Games with Learning

Xin Guo, Renyuan Xu, Thaleia Zariphopoulou

Entropy regularization has been extensively adopted to improve the efficiency, the stability, and the convergence of algorithms in reinforcement learning. This paper analyzes both…

math.OC2020★ 2 cited

Interbank lending with benchmark rates: Pareto optima for a class of singular control games

Rama Cont, Xin Guo, Renyuan Xu

We analyze a class of stochastic differential games of singular control, motivated by the study of a dynamic model of interbank lending with benchmark rates. We describe Pareto opt…

math.OC2019★ 16 cited

Dynamic Programming Principles for Mean-Field Controls with Learning

Haotian Gu, Xin Guo, Xiaoli Wei +1

Dynamic programming principle (DPP) is fundamental for control and optimization, including Markov decision problems (MDPs), reinforcement learning (RL), and more recently mean-fiel…

math.OC2019★ 7 cited

Learning Mean-Field Games

Xin Guo, Anran Hu, Renyuan Xu +1

This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the e…

math.OC2018★ 7 cited

Stochastic Games for Fuel Followers Problem: N vs MFG

Xin Guo, Renyuan Xu

In this paper we formulate and analyze an -player stochastic game of the classical fuel follower problem and its Mean Field Game (MFG) counterpart. For the -player game, we o…