Multi-agent Dynamic Pricing Using Reinforcement Learning and Asymmetric Information

Alexander Kastius (), Nils Kiele () and Rainer Schlosser ()
Additional contact information
Alexander Kastius: University of Potsdam
Nils Kiele: University of Potsdam
Rainer Schlosser: University of Potsdam

Chapter Chapter 66 in Operations Research Proceedings 2022, 2023, pp 557-563 from Springer

Abstract: 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.

Keywords: Dynamic pricing; Reinforcement learning; Multi-agent systems; Asymmetric information (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-24907-5_66

Ordering information: This item can be ordered from
http://www.springer.com/9783031249075

DOI: 10.1007/978-3-031-24907-5_66

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().