6 citations · 11 across the 6 of their papers we have counts for
7 papers · 1 filter
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…
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…