| Summary: | In optimal stopping problems, the objective is the acquisition of a time to take a particular action, in
order to minimize an expected cost. To properly capture that stopping time, however, one has to
compute the value function, this computation is performed numerically and suffers from the curse of
dimensionality. We attempt to overcome the curse of dimensionality by using Neural Networks for
the approximation of the value function. We investigate a particular case of the optimal stopping
problems, infinite horizon markov optimal stopping problems and we show that Neural Networks
can efficiently approximate the Value function even in a high-dimensional setting where the
underlying probability densities may be absent. We conclude by providing an application of our
method on adaptive filtering in a sequential estimation setting.
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