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.

本研究回顾了129篇关于神经蒙特卡罗树搜索方法在除游戏外的领域应用的同行评审文章，旨在系统评估此类方法在实践中的构造方式，以及它们在其他领域中是否成功应用。研究发现这类方法在各种领域中都有应用，采用了多种不同的学习策略和价值函数指导搜索树，并且采用了不同的训练方法。本文扫描了神经蒙特卡罗树搜索算法家族在实践问题中的现有景象，这是针对特定问题及要求设计此类算法的更加原则性的步骤的开端。

超越游戏：神经蒙特卡罗树搜索应用的系统综述