Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Amirhosein Mosavi, Yaser Faghan, Pedram Ghamisi, Puhong Duan, Sina Faizollahzadeh Ardabili, Ely Salwana and Shahab S. Band
Additional contact information
Amirhosein Mosavi: Environmental Quality, Atmospheric Science and Climate Change Research Group, 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
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

Mathematics, 2020, vol. 8, issue 10, 1-42

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.

Keywords: economics; deep reinforcement learning; deep learning; machine learning; mathematics; applied informatics; big data; survey; literature review; explainable artificial intelligence; ensemble; anomaly detection; 5G; fraud detection; COVID-19; Prisma; data science; supervised learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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