Agent-based computational economics is a field with a rich academic history, yet one which has struggled to enter mainstream policy design toolboxes, plagued by the challenges associated with representing a complex and dynamic reality. The field of Reinforcement Learning (RL), too, has a rich history, and has recently been at the centre of several exponential developments. Modern RL implementations have been able to achieve unprecedented levels of sophistication, handling previously unthinkable degrees of complexity. This review surveys the historical barriers of classical agent-based techniques in economic modelling, and contemplates whether recent developments in RL can overcome any of them.

翻译：基于智能体的计算经济学是一个拥有丰富学术历史的领域，但由于难以表征复杂且动态的现实，它一直难以进入主流政策设计工具箱。同样，强化学习领域也历史悠久，并且近来成为多项指数级发展的核心。现代强化学习实现已能达到前所未有的复杂程度，处理先前难以想象的复杂度。本综述梳理了经典基于智能体技术在经济学建模中的历史障碍，并探讨了近期强化学习的发展能否克服其中的一些障碍。