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Learning competitive pricing strategies by multi-agent reinforcement learning


  • Kutschinski, Erich
  • Uthmann, Thomas
  • Polani, Daniel


In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to unforeseen results and increasingly dynamic behavior of the market. In this article we shed some light on price developments arising from a simple price adaptation strategy. Furthermore, we examine several adaptive pricing strategies and their learning behavior in a co-learning scenario with different levels of competition. Q-learning manages to learn best-reply strategies well, but is expensive to train.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:dyncon:v:27:y:2003:i:11:p:2207-2218 DOI: 10.1016/S0165-1889(02)00122-7

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    References listed on IDEAS

    1. Varian, Hal R, 1980. "A Model of Sales," American Economic Review, American Economic Association, vol. 70(4), pages 651-659, September.
    2. Nicolaas J. Vriend, 1996. "A model of market-making," Economics Working Papers 184, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Madhavan, Ananth, 2000. "Market microstructure: A survey," Journal of Financial Markets, Elsevier, vol. 3(3), pages 205-258, August.
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    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

    More about this item


    Distributed simulation; Agent-based computational economics; Dynamic pricing; Multi-agent reinforcement learning; Q-learning;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques


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