Reinforcement Learning for Dynamic Pricing Models : An Adaptive Approach for Optimizing Pricing Strategies

Authors

  • Samuel Johnson  Software Automation Engineer, Lululemon, Seattle, WA, USA

Keywords:

Reinforcement Learning, Dynamic Pricing, Machine Learning, Q-Learning, Deep Q-Networks (DQN), Real-Time Pricing, Customer Segmentation, Competitive Pricing, Data-Driven Strategies, Revenue Optimization

Abstract

E-commerce has focused on dynamic pricing, which employs price changes based on the market's requirements and external conditions. Machine learning, using the reinforcement approach, in particular, supplements the work of developing dynamic pricing strategies that incorporate up-to-the-minute changes. Though adequate when market conditions are stable, traditional pricing strategies cannot predict changes in demand for a product and customers' behavior. RL-based models can handle these gaps as they offer a way to adapt and learn strategies from actual data in real time, which will help increase the accuracy of prices, revenues, and customer satisfaction. This paper discusses the advantages, approaches, and issues of implementing RL in dynamic pricing systems. Data demands, algorithm sophistication, and ethical decisions are invaluable fortifications in practical domains such as retail, airlines, and hospitality. The paper considers RL techniques, including Q-learning and Deep Q-learning networks, in terms of pricing techniques like customer segmentation and competitive pricing. Challenges such as applying RL in future directions as hybrid models and transfer learning show that RL will develop efficient, responsive, and ethical pricing models.

References

  1. Aalto, H. (2019). Competition-Based Dynamic Pricing in E-Commerce (Master's thesis).
  2. Abraham, A. (2005). Rule‐Based expert systems. Handbook of measuring system design.
  3. Agarwal, A., Kakade, S. M., Lee, J. D., & Mahajan, G. (2021). On the theory of policy gradient methods: Optimality, approximation, and distribution shift. Journal of Machine Learning Research, 22(98), 1-76.
  4. Alimi, O. A., Ouahada, K., & Abu-Mahfouz, A. M. (2020). A review of machine learning approaches to power system security and stability. IEEE Access, 8, 113512-113531.
  5. Baktayan, A. A., & Al-Baltah, I. A. (2022). A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions. arXiv preprint arXiv:2208.10072.
  6. Buckley, R., & Caple, J. (2009). The theory and practice of training. Kogan Page Publishers.
  7. Busoniu, L., Babuska, R., & De Schutter, B. (2008). A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 156-172.
  8. Campbell, D., & Frei, F. (2010). Cost structure, customer profitability, and retention implications of self-service distribution channels: Evidence from customer behavior in an online banking channel. Management Science, 56(1), 4-24.
  9. Cerrato, A., & Gitti, G. (2022). Inflation since covid: Demand or supply. Available at SSRN 4193594.
  10. Cervelló-Royo, R., Guijarro, F., & Michniuk, K. (2015). Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data. Expert systems with Applications, 42(14), 5963-5975.
  11. Cramer, C., & Thams, A. (2021). Airline Revenue Management. Springer Books.
  12. Datta, Y. (1996). Market segmentation: An integrated framework. Long Range Planning, 29(6), 797-811.
  13. Dietz, C. V. (2022). Enhancing dynamic pricing in the hotel industry with monotonic constraints: a reinforcement learning approach with Bernstein polynomials.
  14. Gaskett, C., Wettergreen, D., & Zelinsky, A. (1999, December). Q-learning in continuous state and action spaces. In Australasian joint conference on artificial intelligence (pp. 417-428). Berlin, Heidelberg: Springer Berlin Heidelberg.
  15. Gill, A. (2018). Developing A Real-Time Electronic Funds Transfer System for Credit Unions. International Journal of Advanced Research in Engineering and Technology (IJARET), 9(1), 162-184. https://iaeme.com/Home/issue/IJARET?Volume=9&Issue=1
  16. Green, R. J., & Newbery, D. M. (1992). Competition in the British electricity spot market. Journal of political economy, 100(5), 929-953.
  17. Grondman, I., Busoniu, L., Lopes, G. A., & Babuska, R. (2012). A survey of actor-critic reinforcement learning: Standard and natural policy gradients. IEEE Transactions on Systems, Man, and Cybernetics, part C (applications and reviews), 42(6), 1291-1307.
  18. Harrigan, K. R. (1985). Strategic flexibility: A management guide for changing times. Simon and Schuster.
  19. Hennig-Thurau, T. (2000). Relationship marketing: Gaining competitive advantage through customer satisfaction and customer retention. Springer Science & Business Media.
  20. Jayaraman, V., & Baker, T. (2003). The Internet as an enabler for dynamic pricing of goods. IEEE Transactions on Engineering Management, 50(4), 470-477.
  21. Jin, J., Song, C., Li, H., Gai, K., Wang, J., & Zhang, W. (2018, October). Real-time bidding with multi-agent reinforcement learning in display advertising. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 2193-2201).
  22. Johanson, M. B., Hughes, E., Timbers, F., & Leibo, J. Z. (2022). Emergent bartering behaviour in multi-agent reinforcement learning. arXiv preprint arXiv:2205.06760.
  23. Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2020). Leveraging Reinforcement Learning and Bayesian Optimization for Enhanced Dynamic Pricing Strategies. International Journal of AI and ML, 1(3).
  24. Maes, P. (1990). Situated agents can have goals. Robotics and autonomous systems, 6(1-2), 49-70.
  25. Nyati, S. (2018). Revolutionizing LTL Carrier Operations: A Comprehensive Analysis of an Algorithm-Driven Pickup and Delivery Dispatching Solution. International Journal of Science and Research (IJSR), 7(2), 1659-1666. https://www.ijsr.net/getabstract.php?paperid=SR24203183637
  26. Nyati, S. (2018). Transforming Telematics in Fleet Management: Innovations in Asset Tracking, Efficiency, and Communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. https://www.ijsr.net/getabstract.php?paperid=SR24203184230
  27. Rădulescu, R., Mannion, P., Roijers, D. M., & Nowé, A. (2020). Multi-objective multi-agent decision making: a utility-based analysis and survey. Autonomous Agents and Multi-Agent Systems, 34(1), 10.
  28. Ranaweera, C., & Prabhu, J. (2003). The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. International journal of service industry management, 14(4), 374-395.
  29. Schwind, M. (2007). Dynamic pricing and automated resource allocation for complex information services: Reinforcement learning and combinatorial auctions (Vol. 589). Springer Science & Business Media.
  30. Seele, P., Dierksmeier, C., Hofstetter, R., & Schultz, M. D. (2021). Mapping the ethicality of algorithmic pricing: A review of dynamic and personalized pricing. Journal of Business Ethics, 170, 697-719.
  31. Smith, N. C., Goldstein, D. G., & Johnson, E. J. (2013). Choice without awareness: Ethical and policy implications of defaults. Journal of Public Policy & Marketing, 32(2), 159-172.
  32. Smolinska, A., Hauschild, A. C., Fijten, R. R. R., Dallinga, J. W., Baumbach, J., & Van Schooten, F. J. (2014). Current breathomics—a review on data pre-processing techniques and machine learning in metabolomics breath analysis. Journal of breath research, 8(2), 027105.
  33. Taherian, H., Aghaebrahimi, M. R., Baringo, L., & Goldani, S. R. (2021). Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid. International Journal of Electrical Power & Energy Systems, 130, 107004.
  34. Taylor, C., Pollard, S., Rocks, S., & Angus, A. (2012). Selecting policy instruments for better environmental regulation: a critique and future research agenda. Environmental policy and governance, 22(4), 268-292.
  35. Wang, J., Zhang, Y., Kim, T. K., & Gu, Y. (2020, April). Shapley Q-value: A local reward approach to solve global reward games. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 7285-7292).
  36. Zhang, K., Yang, Z., & Başar, T. (2021). Multi-agent reinforcement learning: A selective overview of theories and algorithms. Handbook of reinforcement learning and control, 321-384.
  37. Zhao, R., Hu, Y., Dotzel, J., De Sa, C., & Zhang, Z. (2019, May). Improving neural network quantization without retraining using outlier channel splitting. In International conference on machine learning (pp. 7543-7552). PMLR.
  38. Zong, Z., Wang, H., Wang, J., Zheng, M., & Li, Y. (2022, August). Rbg: Hierarchically solving large-scale routing problems in logistic systems via reinforcement learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4648-4658).

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-11-30

Issue

Volume 8, Issue 6, November-December-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Samuel Johnson, " Reinforcement Learning for Dynamic Pricing Models : An Adaptive Approach for Optimizing Pricing Strategies" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.691-711, November-December-2022.