29 citations · 119 across the 29 of their papers we have counts for
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math.OC2019★ 29 cited
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…
math.OC2019★ 10 cited
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…