@Article{EAJAM-10-4,
author = {Richard, Archibald and Feng, Bao and Yong, Jiongmin},
title = {A Stochastic Gradient Descent Approach for Stochastic Optimal Control},
journal = {East Asian Journal on Applied Mathematics},
year = {2020},
volume = {10},
number = {4},
pages = {635--658},
abstract = {<p style="text-align: justify;">In this work, we introduce a stochastic gradient descent approach to solve
the stochastic optimal control problem through stochastic maximum principle. The motivation that drives our method is the gradient of the cost functional in the stochastic
optimal control problem is under expectation, and numerical calculation of such an expectation requires fully computation of a system of forward backward stochastic differential equations, which is computationally expensive. By evaluating the expectation
with single-sample representation as suggested by the stochastic gradient descent type
optimisation, we could save computational efforts in solving FBSDEs and only focus on
the optimisation task which aims to determine the optimal control process.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.190420.200420},
url = {https://global-sci.com/article/82574/a-stochastic-gradient-descent-approach-for-stochastic-optimal-control}
}