Reinforcement Learning in Macroeconomic Policy Design: A New Frontier?

• URL: http://arxiv.org/abs/2206.08781v1
• Date: Thu, 16 Jun 2022 10:35:26 GMT
• Title: Reinforcement Learning in Macroeconomic Policy Design: A New Frontier?
• Authors: Callum Tilbury
• Abstract summary: Agent-based computational macroeconomics has struggled to enter mainstream policy design toolboxes. Reinforcement Learning has recently been at the centre of several exponential developments. Modern RL implementations have been able to achieve unprecedented levels of sophistication.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agent-based computational macroeconomics 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 macroeconomic modelling, and contemplates whether recent developments in RL can overcome any of them.

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