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Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects
Author:
Tohid Atashbar
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Rui Aruhan Shi
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Series:
IMF Working Papers
Volume/Issue:
Volume 2022: Issue 259
Language:
English
Publication Date:
16 Dec 2022
DOI:
https://doi.org/10.5089/9798400224713.001
ISBN:
9798400224713
ISSN:
1018-5941
Pages:
32
Keywords:
Reinforcement learning; Deep reinforcement learning; Artificial intelligence, RL; DRL; Learning algorithms; Macro modeling; RL algorithm overview; trust region policy optimization; DRL algorithm; decision process; RL algorithm; Machine learning; Artificial intelligence; Debt relief; General equilibrium models; Global
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IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund