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.

翻译：AlphaGo及其继承者的出现标志着利用人工智能玩游戏的新模式的开始,这是通过将蒙特卡洛树搜索、规划程序和深层次学习结合起来实现的。虽然对游戏领域的影响是不可否认的,但更不清楚的是,在游戏以外的应用中,类似的方法如何有用,以及它们需要如何从最初的方法中加以调整。我们审查了129篇经同行审查的文章,详细介绍了在游戏以外的领域应用神经蒙特卡洛树搜索方法的情况。我们的目标是系统地评估这些方法是如何在实践中形成结构的,是否能够将其成功推广到其他领域。我们发现各种领域的应用,许多不同的方式来指导树木搜索,使用学习过的政策和价值功能,以及各种培训方法。我们的审查描绘了神经Monte carlo树搜索体系中目前用于实际问题的各种算法的格局,这是为具体问题及其要求设计更有原则的算法的第一步。</s>