Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

Author/Editor:

Tohid Atashbar ; Rui Aruhan Shi

Publication Date:

December 16, 2022

Electronic Access:

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Summary:

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.

Series:

Working Paper No. 2022/259

Frequency:

regular

English

Publication Date:

December 16, 2022

ISBN/ISSN:

9798400224713/1018-5941

Stock No:

WPIEA2022259

Pages:

32

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