Deep neural network expressivity for optimal stopping problems
Lukas Gonon
Abstract:This article studies deep neural network expression rates for optimal stopping problems of discrete-time Markov processes on high-dimensional state spaces. A general framework is established in which the value function and continuation value of an optimal stopping problem can be approximated with error at most $\varepsilon $
ε
by a deep ReLU neural network of size at most $\kappa d^{\mathfrak{q}} \varepsilon ^{-\mathfrak{r}}$
κ
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