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Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

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  • Mosavi, Amir
  • Faghan, Yaser
  • Ghamisi, Pedram
  • Duan, Puhong
  • Ardabili, Sina Faizollahzadeh
  • Hassan, Salwana
  • Band, Shahab S.

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

  • Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:jrc58
    DOI: 10.31219/osf.io/jrc58
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    1. Li, Danny H.W. & Lou, Siwei & Lam, Joseph C. & Wu, Ronald H.T., 2016. "Determining solar irradiance on inclined planes from classified CIE (International Commission on Illumination) standard skies," Energy, Elsevier, vol. 101(C), pages 462-470.
    2. Jamil, Basharat & Akhtar, Naiem, 2017. "Estimation of diffuse solar radiation in humid-subtropical climatic region of India: Comparison of diffuse fraction and diffusion coefficient models," Energy, Elsevier, vol. 131(C), pages 149-164.
    3. Martin Hofmann & Gunther Seckmeyer, 2017. "A New Model for Estimating the Diffuse Fraction of Solar Irradiance for Photovoltaic System Simulations," Energies, MDPI, vol. 10(2), pages 1-21, February.
    4. Rojas, Redlich García & Alvarado, Natalia & Boland, John & Escobar, Rodrigo & Castillejo-Cuberos, Armando, 2019. "Diffuse fraction estimation using the BRL model and relationship of predictors under Chilean, Costa Rican and Australian climatic conditions," Renewable Energy, Elsevier, vol. 136(C), pages 1091-1106.
    5. Kontoleon, K.J., 2015. "Glazing solar heat gain analysis and optimization at varying orientations and placements in aspect of distributed radiation at the interior surfaces," Applied Energy, Elsevier, vol. 144(C), pages 152-164.
    6. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2013. "Zero energy buildings and sustainable development implications – A review," Energy, Elsevier, vol. 54(C), pages 1-10.
    7. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    8. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    9. Kong, Chengdong & Xu, Zilin & Yao, Qiang, 2013. "Outdoor performance of a low-concentrated photovoltaic–thermal hybrid system with crystalline silicon solar cells," Applied Energy, Elsevier, vol. 112(C), pages 618-625.
    10. Li, Danny H.W. & Cheung, K.L. & Lam, Tony N.T. & Chan, Wilco W.H., 2012. "A study of grid-connected photovoltaic (PV) system in Hong Kong," Applied Energy, Elsevier, vol. 90(1), pages 122-127.
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