论文标题
对经济政策的强化学习：一个新的边界？
Reinforcement Learning for Economic Policy: A New Frontier?
论文作者
论文摘要
基于代理商的计算经济学是一个具有丰富学术史的领域，但是它一直在努力进入主流政策设计工具箱，这与代表复杂而动态的现实相关的挑战所困扰。加固学习领域（RL）也有悠久的历史，最近是几个指数发展的中心。现代的RL实现已经能够达到前所未有的复杂水平，从而处理了以前无法想象的复杂程度。这篇评论调查了经济建模中基于经典代理的技术的历史障碍，并考虑了RL的最新发展是否可以克服任何一个。
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.
