Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

Tohid Atashbar and Rui Aruhan Shi

No 2022/259, IMF Working Papers from International Monetary Fund

Abstract: 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: 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 (search for similar items in EconPapers)
Pages: 32
Date: 2022-12-16
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (4)

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