TY - BOOK ID - 136587866 TI - Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects AU - Atashbar, Tohid. AU - Aruhan Shi, Rui. PY - 2022 SN - 9798400229909 PB - Washington, D.C. : International Monetary Fund, DB - UniCat KW - Macroeconomics KW - Economics: General KW - Intelligence (AI) & Semantics KW - Environmental Economics KW - Financial Risk Management KW - Econometrics KW - Computational Techniques KW - Data Collection and Data Estimation Methodology KW - Computer Programs: Other KW - Network Formation and Analysis: Theory KW - Technological Change: Choices and Consequences KW - Diffusion Processes KW - Environmental Economics: General KW - Debt KW - Debt Management KW - Sovereign Debt KW - Computable and Other Applied General Equilibrium Models KW - Economic & financial crises & disasters KW - Economics of specific sectors KW - Machine learning KW - Environmental economics KW - Artificial intelligence KW - Finance KW - Econometrics & economic statistics KW - Technology KW - Debt relief KW - Asset and liability management KW - General equilibrium models KW - Econometric analysis KW - Currency crises KW - Informal sector KW - Economics KW - Environmental sciences KW - Debts, External KW - Econometric models UR - https://www.unicat.be/uniCat?func=search&query=sysid:136587866 AB - 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. ER -