IMF Working Papers

Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

By Tohid Atashbar, Rui Aruhan Shi

December 16, 2022

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Tohid Atashbar, and Rui Aruhan Shi. Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects, (USA: International Monetary Fund, 2022) accessed November 4, 2024

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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.

Keywords: RL, Artificial intelligence, Deep reinforcement learning, DRL, Learning algorithms, Macro modeling, Reinforcement learning

Publication Details

  • Pages:

    32

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2022/259

  • Stock No:

    WPIEA2022259

  • ISBN:

    9798400224713

  • ISSN:

    1018-5941