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Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics

Citations

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Cited by:

  1. Amir Masoud Rahmani & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Zahid Mehmood & Amir Haider & Mehdi Hosseinzadeh & Rizwan Ali Naqvi, 2021. "Machine Learning (ML) in Medicine: Review, Applications, and Challenges," Mathematics, MDPI, vol. 9(22), pages 1-52, November.
  2. Muhammad Umar Khan & Somia Mehak & Dr. Wajiha Yasir & Shagufta Anwar & Muhammad Usman Majeed & Hafiz Arslan Ramzan, 2023. "Quantitative Studies Of Deep Reinforcement Learning In Gaming, Robotics And Real-World Control Systems," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 12(2), pages 389-395.
  3. Brini, Alessio & Tedeschi, Gabriele & Tantari, Daniele, 2023. "Reinforcement learning policy recommendation for interbank network stability," Journal of Financial Stability, Elsevier, vol. 67(C).
  4. Charl Maree & Christian W. Omlin, 2022. "Balancing Profit, Risk, and Sustainability for Portfolio Management," Papers 2207.02134, arXiv.org.
  5. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
  6. Fernando Loor & Veronica Gil-Costa & Mauricio Marin, 2024. "Metric Space Indices for Dynamic Optimization in a Peer to Peer-Based Image Classification Crowdsourcing Platform," Future Internet, MDPI, vol. 16(6), pages 1-29, June.
  7. Shidi Deng & Maximilian Schiffer & Martin Bichler, 2024. "Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning," Papers 2406.02437, arXiv.org.
  8. Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
  9. Adrian Millea & Abbas Edalat, 2022. "Using Deep Reinforcement Learning with Hierarchical Risk Parity for Portfolio Optimization," IJFS, MDPI, vol. 11(1), pages 1-16, December.
  10. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
  11. Tian Zhu & Wei Zhu, 2022. "Quantitative Trading through Random Perturbation Q-Network with Nonlinear Transaction Costs," Stats, MDPI, vol. 5(2), pages 1-15, June.
  12. Xiao-Shan Chen & Min Gyeong Kim & Chi-Ho Lin & Hyung Jong Na, 2025. "Development of per Capita GDP Forecasting Model Using Deep Learning: Including Consumer Goods Index and Unemployment Rate," Sustainability, MDPI, vol. 17(3), pages 1-28, January.
  13. Horobet, Alexandra & Boubaker, Sabri & Belascu, Lucian & Negreanu, Cristina Carmencita & Dinca, Zeno, 2024. "Technology-driven advancements: Mapping the landscape of algorithmic trading literature," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
  14. Chien-Liang Chiu & Paoyu Huang & Min-Yuh Day & Yensen Ni & Yuhsin Chen, 2024. "Mastery of “Monthly Effects”: Big Data Insights into Contrarian Strategies for DJI 30 and NDX 100 Stocks over a Two-Decade Period," Mathematics, MDPI, vol. 12(2), pages 1-21, January.
  15. Jifan Zhang & Salih Tutun & Samira Fazel Anvaryazdi & Mohammadhossein Amini & Durai Sundaramoorthi & Hema Sundaramoorthi, 2024. "Management of resource sharing in emergency response using data-driven analytics," Annals of Operations Research, Springer, vol. 339(1), pages 663-692, August.
  16. Valentin Kuleto & Milena Ilić & Mihail Dumangiu & Marko Ranković & Oliva M. D. Martins & Dan Păun & Larisa Mihoreanu, 2021. "Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions," Sustainability, MDPI, vol. 13(18), pages 1-16, September.
  17. Rui (Aruhan) Shi, 2021. "Learning from Zero: How to Make Consumption-Saving Decisions in a Stochastic Environment with an AI Algorithm," CESifo Working Paper Series 9255, CESifo.
  18. Reilly Pickard & Yuri Lawryshyn, 2023. "Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review," Mathematics, MDPI, vol. 11(24), pages 1-19, December.
  19. Petr Suler & Zuzana Rowland & Tomas Krulicky, 2021. "Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China," JRFM, MDPI, vol. 14(2), pages 1-30, February.
  20. Bruno Gašperov & Stjepan Begušić & Petra Posedel Šimović & Zvonko Kostanjčar, 2021. "Reinforcement Learning Approaches to Optimal Market Making," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
  21. Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
  22. Dimitrios Vamvakas & Panagiotis Michailidis & Christos Korkas & Elias Kosmatopoulos, 2023. "Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications," Energies, MDPI, vol. 16(14), pages 1-38, July.
  23. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
  24. Rui & Shi, 2021. "Learning from zero: how to make consumption-saving decisions in a stochastic environment with an AI algorithm," Papers 2105.10099, arXiv.org, revised Feb 2022.
  25. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.
  26. Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.
  27. Callum Rhys Tilbury, 2022. "Reinforcement Learning for Economic Policy: A New Frontier?," Papers 2206.08781, arXiv.org, revised Feb 2023.
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