Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
| Autor: | Pedram Ghamisi, Yaser Faghan, Puhong Duan, Sina Ardabili, Shahab S. Band, Ely Salwana, Amirhosein Mosavi |
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| Rok vydání: | 2020 |
| Předmět: |
fraud detection
Computer science bepress|Engineering literature review Big data 02 engineering and technology computer.software_genre big data 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Reinforcement learning Robustness (economics) mathematics lcsh:QA1-939 BodoArXiv|Time Periods BodoArXiv|Areas or Regions anomaly detection Economic data machine learning engrXiv|Engineering Scalability 020201 artificial intelligence & image processing Profitability index General Mathematics Machine learning supervised learning Robustness (computer science) bepress|Social and Behavioral Sciences|International and Area Studies survey Engineering (miscellaneous) deep reinforcement learning explainable artificial intelligence business.industry applied informatics lcsh:Mathematics Deep learning Supervised learning ensemble deep learning COVID-19 020206 networking & telecommunications economics Range (mathematics) bepress|Arts and Humanities|History Artificial intelligence data science Prisma business computer Mathematics 5G Economic problem |
| Zdroj: | Mathematics Mathematics, Vol 8, Iss 1640, p 1640 (2020) |
| DOI: | 10.31219/osf.io/jrc58 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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