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Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

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  • Amirhosein Mosavi

    (Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Yaser Faghan

    (Instituto Superior de Economia e Gestao, University of Lisbon, 1200-781 Lisbon, Portugal)

  • Pedram Ghamisi

    (Helmholtz-Zentrum Dresden-Rossendorf, Chemnitzer Str. 40, D-09599 Freiberg, Germany
    Department of Physics, Faculty of Science, the University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium)

  • Puhong Duan

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Sina Faizollahzadeh Ardabili

    (Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran)

  • Ely Salwana

    (Institute of IR4.0, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Shahab S. Band

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

Abstract

The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties.

Suggested Citation

  • Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1640-:d:417900
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    References listed on IDEAS

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