IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v21y2022i1d10.1057_s41272-021-00285-3.html
   My bibliography  Save this article

Dynamic pricing under competition using reinforcement learning

Author

Listed:
  • Alexander Kastius

    (University of Potsdam)

  • Rainer Schlosser

    (University of Potsdam)

Abstract

Dynamic pricing is considered a possibility to gain an advantage over competitors in modern online markets. The past advancements in Reinforcement Learning (RL) provided more capable algorithms that can be used to solve pricing problems. In this paper, we study the performance of Deep Q-Networks (DQN) and Soft Actor Critic (SAC) in different market models. We consider tractable duopoly settings, where optimal solutions derived by dynamic programming techniques can be used for verification, as well as oligopoly settings, which are usually intractable due to the curse of dimensionality. We find that both algorithms provide reasonable results, while SAC performs better than DQN. Moreover, we show that under certain conditions, RL algorithms can be forced into collusion by their competitors without direct communication.

Suggested Citation

  • Alexander Kastius & Rainer Schlosser, 2022. "Dynamic pricing under competition using reinforcement learning," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(1), pages 50-63, February.
    • Handle: RePEc:pal:jorapm:v:21:y:2022:i:1:d:10.1057_s41272-021-00285-3
    DOI: 10.1057/s41272-021-00285-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-021-00285-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-021-00285-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rana, Rupal & Oliveira, Fernando S., 2014. "Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning," Omega, Elsevier, vol. 47(C), pages 116-126.
    2. Kutschinski, Erich & Uthmann, Thomas & Polani, Daniel, 2003. "Learning competitive pricing strategies by multi-agent reinforcement learning," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11), pages 2207-2218.
    3. Kutschinski, Erich & Uthmann, Thomas & Polani, Daniel, 2003. "Learning competitive pricing strategies by multi-agent reinforcement learning," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11-12), pages 2207-2218, September.
    4. Nicolas Bondoux & Anh Quan Nguyen & Thomas Fiig & Rodrigo Acuna-Agost, 2020. "Reinforcement learning applied to airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 332-348, October.
    5. Giannoccaro, Ilaria & Pontrandolfo, Pierpaolo, 2002. "Inventory management in supply chains: a reinforcement learning approach," International Journal of Production Economics, Elsevier, vol. 78(2), pages 153-161, July.
    6. R. Schlosser & K. Richly, 2019. "Dynamic pricing under competition with data-driven price anticipations and endogenous reference price effects," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(6), pages 451-464, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jan Groeneveld & Judith Herrmann & Nikkel Mollenhauer & Leonard Dreeßen & Nick Bessin & Johann Schulze Tast & Alexander Kastius & Johannes Huegle & Rainer Schlosser, 2024. "Self-learning Agents for Recommerce Markets," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(4), pages 441-463, August.
    2. Yu Xia & Ali Arian & Sriram Narayanamoorthy & Joshua Mabry, 2023. "RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation," Papers 2312.14095, arXiv.org.
    3. Torsten J. Gerpott & Jan Berends, 2022. "Competitive pricing on online markets: a literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 596-622, December.
    4. Shidi Deng & Maximilian Schiffer & Martin Bichler, 2024. "Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning," Papers 2406.02437, arXiv.org.
    5. Justin Dumouchelle & Emma Frejinger & Andrea Lodi, 2024. "Reinforcement learning for freight booking control problems," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(4), pages 318-345, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Torsten J. Gerpott & Jan Berends, 2022. "Competitive pricing on online markets: a literature review," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 596-622, December.
    2. Tong Zhang & B. Brorsen, 2011. "Oligopoly firms with quantity-price strategic decisions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(2), pages 157-170, November.
    3. Ruben Geer & Arnoud V. Boer & Christopher Bayliss & Christine S. M. Currie & Andria Ellina & Malte Esders & Alwin Haensel & Xiao Lei & Kyle D. S. Maclean & Antonio Martinez-Sykora & Asbjørn Nilsen Ris, 2019. "Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(3), pages 185-203, June.
    4. Leonardo Bargigli & Gabriele Tedeschi, 2013. "Major trends in agent-based economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(2), pages 211-217, October.
    5. Moreno-Izquierdo, Luis & Egorova, Galina & Peretó-Rovira, Alexandre & Más-Ferrando , Adrián, 2018. "Exploring the use of artificial intelligence in price maximisation in the tourism sector: its application in the case of Airbnb in the Valencian Community," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 42, pages 113-128.
    6. Ахмадеев Б.А.* & Макаров В.Л.**, 2019. "Система Оценки Проектов На Основе Комбинированных Методов Компьютерной Оптимизации," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 55(2), pages 5-23, апрель.
    7. Ruben van de Geer & Arnoud V. den Boer & Christopher Bayliss & Christine Currie & Andria Ellina & Malte Esders & Alwin Haensel & Xiao Lei & Kyle D. S. Maclean & Antonio Martinez-Sykora & Asbj{o}rn Nil, 2018. "Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference," Papers 1804.03219, arXiv.org.
    8. Tharakunnel, Kurian & Bhattacharyya, Siddhartha, 2009. "Single-leader-multiple-follower games with boundedly rational agents," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1593-1603, August.
    9. Tong Zhang & B. Brorsen, 2009. "Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 34(4), pages 399-417, November.
    10. Yan, Yimo & Chow, Andy H.F. & Ho, Chin Pang & Kuo, Yong-Hong & Wu, Qihao & Ying, Chengshuo, 2022. "Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    11. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    12. Justin Dumouchelle & Emma Frejinger & Andrea Lodi, 2024. "Reinforcement learning for freight booking control problems," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(4), pages 318-345, August.
    13. Zhang, Tong & Brorsen, B. Wade, 2008. "Price Competition with Particle Swarm Optimization: An Agent-Based Artificial Model," 2008 Annual Meeting, February 2-6, 2008, Dallas, Texas 6780, Southern Agricultural Economics Association.
    14. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2021. "Multi-unit multiple bid auctions in balancing markets: An agent-based Q-learning approach," Energy Economics, Elsevier, vol. 93(C).
    15. Marco Raberto & Andrea Teglio & Silvano Cincotti, 2008. "Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 147-162, September.
    16. Callum Rhys Tilbury, 2022. "Reinforcement Learning for Economic Policy: A New Frontier?," Papers 2206.08781, arXiv.org, revised Feb 2023.
    17. Junyi Xu, 2021. "Reinforcement Learning in a Cournot Oligopoly Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1001-1024, December.
    18. Yi Zheng & Zehao Li & Peng Jiang & Yijie Peng, 2024. "Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment," Papers 2410.21109, arXiv.org.
    19. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2018. "Multi-unit multiple bid auctions in balancing markets: an agent-based Q-learning approach," EWI Working Papers 2018-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    20. Azadeh, A. & Skandari, M.R. & Maleki-Shoja, B., 2010. "An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation," Energy Policy, Elsevier, vol. 38(10), pages 6307-6319, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:21:y:2022:i:1:d:10.1057_s41272-021-00285-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.