  The popularity of deep reinforcement learning (DRL) methods in economics have
been exponentially increased. DRL through a wide range of capabilities from
reinforcement learning (RL) and deep learning (DL) for handling sophisticated
dynamic business environments offers vast opportunities. DRL is characterized
by scalability with the potential to be applied to high-dimensional problems in
conjunction with noisy and nonlinear patterns of economic data. In this work,
we first consider a brief review of DL, RL, and deep RL methods in diverse
applications in economics providing an in-depth insight into the state of the
art. Furthermore, the architecture of DRL applied to economic applications is
investigated in order to highlight the complexity, robustness, accuracy,
performance, computational tasks, risk constraints, and profitability. The
survey results indicate that DRL can provide better performance and higher
accuracy as compared to the traditional algorithms while facing real economic
problems at the presence of risk parameters and the ever-increasing
uncertainties.
