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The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.

Macroeconomics --- Economics: General --- Intelligence (AI) & Semantics --- Environmental Economics --- Financial Risk Management --- Econometrics --- Computational Techniques --- Data Collection and Data Estimation Methodology --- Computer Programs: Other --- Network Formation and Analysis: Theory --- Technological Change: Choices and Consequences --- Diffusion Processes --- Environmental Economics: General --- Debt --- Debt Management --- Sovereign Debt --- Computable and Other Applied General Equilibrium Models --- Economic & financial crises & disasters --- Economics of specific sectors --- Machine learning --- Environmental economics --- Artificial intelligence --- Finance --- Econometrics & economic statistics --- Technology --- Debt relief --- Asset and liability management --- General equilibrium models --- Econometric analysis --- Currency crises --- Informal sector --- Economics --- Environmental sciences --- Debts, External --- Econometric models