Reinforcement Learning for Economic Policy: A New Frontier?

TL;DR

This paper reviews how recent advances in reinforcement learning could overcome historical barriers faced by agent-based models in economic policy design, potentially transforming the field.

Contribution

It analyzes the potential of modern reinforcement learning to address longstanding challenges in agent-based economic modeling.

Findings

RL achieves higher complexity handling in economic models

Recent RL developments may enable practical policy design tools

Historical barriers in agent-based economics are being addressed by RL

Abstract

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.