29 citations · 119 across the 29 of their papers we have counts for
4 papers · 1 filter
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…