  The advent of AlphaGo and its successors marked the beginning of a new
paradigm in playing games using artificial intelligence. This was achieved by
combining Monte Carlo tree search, a planning procedure, and deep learning.
While the impact on the domain of games has been undeniable, it is less clear
how useful similar approaches are in applications beyond games and how they
need to be adapted from the original methodology. We review 129 peer-reviewed
articles detailing the application of neural Monte Carlo tree search methods in
domains other than games. Our goal is to systematically assess how such methods
are structured in practice and if their success can be extended to other
domains. We find applications in a variety of domains, many distinct ways of
guiding the tree search using learned policy and value functions, and various
training methods. Our review maps the current landscape of algorithms in the
family of neural monte carlo tree search as they are applied to practical
problems, which is a first step towards a more principled way of designing such
algorithms for specific problems and their requirements.
