29 citations · 119 across the 29 of their papers we have counts for
30 papers
Modeling Stochastic Multi-Agent Interaction in Intraday Battery Energy Storage Dispatch with Market Power
Ruimeng Hu, Mike Ludkovski, Hezhong Zhang
We develop a stochastic game-theoretic model for intraday dispatch of grid-scale battery energy storage systems (BESSs). We assume that each BESS operator competitively manages her…
An Actor-Critic Framework for Continuous-Time Jump-Diffusion Controls with Normalizing Flows
Liya Guo, Ruimeng Hu, Xu Yang +1
Continuous-time stochastic control with time-inhomogeneous jump-diffusion dynamics is central in finance and economics, but computing optimal policies is difficult under explicit t…
Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces
Andrea Angiuli, Jean-Pierre Fouque, Ruimeng Hu +1
We present the development and analysis of a reinforcement learning (RL) algorithm designed to solve continuous-space mean field game (MFG) and mean field control (MFC) problems in…
Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms
Robert Balkin, Hector D. Ceniceros, Ruimeng Hu
In this paper, we propose a numerical methodology for finding the closed-loop Nash equilibrium of stochastic delay differential games through deep learning. These games are prevale…
Directed Chain Generative Adversarial Networks
Ming Min, Ruimeng Hu, Tomoyuki Ichiba
Real-world data can be multimodal distributed, e.g., data describing the opinion divergence in a community, the interspike interval distribution of neurons, and the oscillators nat…
Recent Developments in Machine Learning Methods for Stochastic Control and Games
Ruimeng Hu, Mathieu Laurière
Stochastic optimal control and games have a wide range of applications, from finance and economics to social sciences, robotics, and energy management. Many real-world applications…
Pathwise Solutions for Stochastic Hydrostatic Euler Equations under the Local Rayleigh Condition
Ruimeng Hu, Quyuan Lin
The hydrostatic Euler equations are important in the study of atmospheric and oceanic dynamics in the planetary scale. While its deterministic version has been widely studied in th…
Convergence of the Backward Deep BSDE Method with Applications to Optimal Stopping Problems
Chengfan Gao, Siping Gao, Ruimeng Hu +1
The optimal stopping problem is one of the core problems in financial markets, with broad applications such as pricing American and Bermudan options. The deep BSDE method [Han, Jen…
Higher-Order error estimates for physics-informed neural networks approximating the primitive equations
Ruimeng Hu, Quyuan Lin, Alan Raydan +1
Large-scale dynamics of the oceans and the atmosphere are governed by primitive equations (PEs). Due to the nonlinearity and nonlocality, the numerical study of the PEs is generall…
Pandemic Control, Game Theory and Machine Learning
Yao Xuan, Robert Balkin, Jiequn Han +2
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus…
Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence
Jiequn Han, Ruimeng Hu, Jihao Long
One of the core problems in mean-field control and mean-field games is to solve the corresponding McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs). Most…
Systemic Risk Models for Disjoint and Overlapping Groups with Equilibrium Strategies
Yichen Feng, Jean-Pierre Fouque, Ruimeng Hu +1
We analyze the systemic risk for disjoint and overlapping groups (e.g., central clearing counterparties (CCP)) by proposing new models with realistic game features. Specifically, w…
Local Martingale Solutions and Pathwise Uniqueness for the Three-dimensional Stochastic Inviscid Primitive Equations
Ruimeng Hu, Quyuan Lin
We study the stochastic effect on the three-dimensional inviscid primitive equations (PEs, also called the hydrostatic Euler equations). Specifically, we consider a larger class of…
-player and Mean-field Games in Itô-diffusion Markets with Competitive or Homophilous Interaction
Ruimeng Hu, Thaleia Zariphopoulou
In Itô-diffusion environments, we introduce and analyze -player and common-noise mean-field games in the context of optimal portfolio choice in a common market. The players inve…
Signatured Deep Fictitious Play for Mean Field Games with Common Noise
Ming Min, Ruimeng Hu
Existing deep learning methods for solving mean-field games (MFGs) with common noise fix the sampling common noise paths and then solve the corresponding MFGs. This leads to a nest…
Sub- and Super-solution Approach to Accuracy Analysis of Portfolio Optimization Asymptotics in Multiscale Stochastic Factor Market
Jean-Pierre Fouque, Ruimeng Hu, Ronnie Sircar
The problem of portfolio optimization when stochastic factors drive returns and volatilities has been studied in previous works by the authors. In particular, they proposed asympto…
A Class of Dimension-free Metrics for the Convergence of Empirical Measures
Jiequn Han, Ruimeng Hu, Jihao Long
This paper concerns the convergence of empirical measures in high dimensions. We propose a new class of probability metrics and show that under such metrics, the convergence is fre…
Recurrent Neural Networks for Stochastic Control Problems with Delay
Jiequn Han, Ruimeng Hu
Stochastic control problems with delay are challenging due to the path-dependent feature of the system and thus its intrinsic high dimensions. In this paper, we propose and systema…
Optimal Policies for a Pandemic: A Stochastic Game Approach and a Deep Learning Algorithm
Yao Xuan, Robert Balkin, Jiequn Han +2
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this paper, we propose a multi-reg…
Convergence of Deep Fictitious Play for Stochastic Differential Games
Jiequn Han, Ruimeng Hu, Jihao Long
Stochastic differential games have been used extensively to model agents' competitions in Finance, for instance, in P2P lending platforms from the Fintech industry, the banking sys…
Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games
Jiequn Han, Ruimeng Hu
We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large -player stochastic differential games. Following the idea of fictiti…
Deep Fictitious Play for Stochastic Differential Games
Ruimeng Hu
In this paper, we apply the idea of fictitious play to design deep neural networks (DNNs), and develop deep learning theory and algorithms for computing the Nash equilibrium of asy…
Multiscale Asymptotic Analysis for Portfolio Optimization under Stochastic Environment
Jean-Pierre Fouque, Ruimeng Hu
Empirical studies indicate the presence of multi-scales in the volatility of underlying assets: a fast-scale on the order of days and a slow-scale on the order of months. In our pr…
Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems
Ruimeng Hu
In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics. The problem of ranking re…