16 citations · 101 across the 22 of their papers we have counts for
10 papers · 1 filter
Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators
Yinbin Han, Meisam Razaviyayn, Renyuan Xu
Nonlinear control systems with partial information to the decision maker are prevalent in a variety of applications. As a step toward studying such nonlinear systems, this work exp…
Asymptotic Analysis of Deep Residual Networks
Rama Cont, Alain Rossier, Renyuan Xu
We investigate the asymptotic properties of deep Residual networks (ResNets) as the number of layers increases. We first show the existence of scaling regimes for trained weights m…
Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing
Jingwei Ji, Renyuan Xu, Ruihao Zhu
Motivated by practical considerations in machine learning for financial decision-making, such as risk aversion and large action space, we consider risk-aware bandits optimization w…
Convergence and Implicit Regularization Properties of Gradient Descent for Deep Residual Networks
Rama Cont, Alain Rossier, RenYuan Xu
We prove linear convergence of gradient descent to a global optimum for the training of deep residual networks with constant layer width and smooth activation function. We show tha…
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
Haotian Gu, Xin Guo, Xiaoli Wei +1
One of the challenges for multi-agent reinforcement learning (MARL) is designing efficient learning algorithms for a large system in which each agent has only limited or partial in…
Scaling Properties of Deep Residual Networks
Alain-Sam Cohen, Rama Cont, Alain Rossier +1
Residual networks (ResNets) have displayed impressive results in pattern recognition and, recently, have garnered considerable theoretical interest due to a perceived link with neu…
Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon
Ben Hambly, Renyuan Xu, Huining Yang
We explore reinforcement learning methods for finding the optimal policy in the linear quadratic regulator (LQR) problem. In particular, we consider the convergence of policy gradi…
Delay-Adaptive Learning in Generalized Linear Contextual Bandits
Jose Blanchet, Renyuan Xu, Zhengyuan Zhou
In this paper, we consider online learning in generalized linear contextual bandits where rewards are not immediately observed. Instead, rewards are available to the decision-maker…
A General Framework for 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…
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Haotian Gu, Xin Guo, Xiaoli Wei +1
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality. This paper builds the mathematical framework to a…