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Multi-agent Dynamic Pricing Using Reinforcement Learning and Asymmetric Information

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Operations Research Proceedings 2022 (OR 2022)

Part of the book series: Lecture Notes in Operations Research ((LNOR))

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Abstract

Self-learning agents can be used in numerous ways for dynamic pricing nowadays. It has been shown, that reinforcement learning can serve as a toolkit to efficiently develop pricing strategies in dynamic environments. In many real-world situations, it can be expected that multiple market participants rely on such self-learning agents to implement pricing decisions. From the view of one agent, this violates the fundamental Markov property, which leads to instability in the learning process. Past publications proposed to rely on asymmetric information to achieve equilibria and usually focused on tabular solutions or solvers. We use multi-agent learning and asymmetric information with function approximation tools for high-dimensional state spaces by exchanging policy information between multiple actors. We discuss possible problems and their solutions and propose a simulation environment for further evaluation of the developed system.

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References

  1. Goodfellow, I. J., Bengio, Y., & Courville, A. C. (2016). Deep learning. In Adaptive computation and machine learning. MIT Press.

    Google Scholar 

  2. Kastius, A., & Schlosser, R. (2022). Dynamic pricing under competition using reinforcement learning. Journal of Revenue and Pricing Management, 21, 50–63.

    Article  Google Scholar 

  3. Könönen, V. (2003). Gradient based method for symmetric and asymmetric multiagent reinforcement learning. In IDEAL 2003, revised papers. Lecture Notes in Computer Science (Vol. 2690, pp. 68–75). Springer.

    Google Scholar 

  4. Könönen, V. (2004). Asymmetric multiagent reinforcement learning. Web Intelligence and Agent Systems, 2(2), 105–121.

    Google Scholar 

  5. Könönen, V. (2006). Dynamic pricing based on asymmetric multiagent reinforcement learning. International Journal of Intelligent Systems, 21(1), 73–98.

    Article  Google Scholar 

  6. Kulkarni, T. D., Narasimhan, K., Saeedi, A., & Tenenbaum, J. (2016). Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. In NIPS 2016 (pp. 3675–3683).

    Google Scholar 

  7. Le, T. P., Vien, N. A., & Chung, T. (2018). A deep hierarchical reinforcement learning algorithm in partially observable Markov decision processes. IEEE Access, 6, 49089–49102.

    Google Scholar 

  8. Pereira, S. (2020). Stackelberg multi-agent reinforcement learning for hierarchical environments [Master’s thesis]. http://hdl.handle.net/10012/15851

  9. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning—An introduction. In Adaptive computation and machine learning. MIT Press.

    Google Scholar 

  10. Tharakunnel, K., & Bhattacharyya, S. (2009). Single-leader-multiple-follower games with boundedly rational agents. Journal of Economic Dynamics and Control, 33, 1593–1603.

    Article  Google Scholar 

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Correspondence to Alexander Kastius .

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Kastius, A., Kiele, N., Schlosser, R. (2023). Multi-agent Dynamic Pricing Using Reinforcement Learning and Asymmetric Information. In: Grothe, O., Nickel, S., Rebennack, S., Stein, O. (eds) Operations Research Proceedings 2022. OR 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-24907-5_66

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