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：随着AlphaGo及其后继者的出现，人工智能在游戏领域开创了一个新范式。这一突破通过将蒙特卡洛树搜索这一规划方法与深度学习相结合而实现。尽管该方法在游戏领域的影响毋庸置疑，但其在游戏以外应用场景中的实用价值以及从原始方法论中需要做出的适应性调整仍不明确。我们系统回顾了129篇同行评审论文，这些论文详细阐述了神经蒙特卡洛树搜索方法在非游戏领域中的应用。我们的目标是系统评估此类方法在实际中的结构特征，并探究其成功经验能否扩展至其他领域。研究发现，该方法已应用于多个领域，形成了多种利用学习策略函数和值函数引导树搜索的独特途径，并发展出丰富的训练方案。本综述描绘了神经蒙特卡洛树搜索算法族在解决实际问题时的当前应用全景，这为针对特定问题及其需求更规范化地设计此类算法奠定了初步基础。