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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
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    References listed on IDEAS

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    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.
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    4. 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.
    5. 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.
    6. 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.
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    Cited by:

    1. Yu Xia & Ali Arian & Sriram Narayanamoorthy & Joshua Mabry, 2023. "RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation," Papers 2312.14095, arXiv.org.
    2. 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.

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