  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.
