29 citations · 119 across the 29 of their papers we have counts for
13 papers · 1 filter
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…
A Continuous-Time Actor-Critic Framework for 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…
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…
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…
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…
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…