Reinforcement Learning for Economic Policy: A New Frontier?
It explores a potential solution for improving economic policy modeling, but it is incremental as it only surveys existing developments without introducing novel methods.
This review examines whether recent advances in reinforcement learning can address the historical challenges of agent-based computational economics in representing complex realities for policy design, but it does not present new results or concrete numbers.
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.