Reinforcement Learning in Macroeconomic Policy Design: A New Frontier?. (arXiv:2206.08781v1 [cs.LG])

Web: http://arxiv.org/abs/2206.08781

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 …

arxiv design learning lg policy reinforcement reinforcement learning