Towards Transfer Learning for Revenue and Pricing Management

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

A chapter in Operations Research Proceedings 2021, 2022, pp 361-366 from Springer

Abstract: Abstract Reinforcement Learning (RL) has proven itself as a powerful tool to optimize pricing processes. With the support of deep non-linear function approximation tools, it can handle complex and continuous state and action spaces. This ability can leverage the utility of pricing algorithms in markets with a vast number of participants or in use cases where additional product features should be considered in the pricing system. One problem with those tools is their apparent demand for training data, which might not be available for a single market. We propose to use techniques instead, that leverage the knowledge of different problems. Several similar algorithms have been proposed in the past years to allow RL algorithms to operate efficiently on various processes simultaneously. DISTRAL continuously merges information from different decision processes towards a distilled policy and uses the joint policy to update the market-specific source policies. We will discuss the influence of such regularization mechanisms. Multi-market pricing problems are used to illustrate their impact.

Date: 2022
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:978-3-031-08623-6_53

Ordering information: This item can be ordered from
http://www.springer.com/9783031086236

DOI: 10.1007/978-3-031-08623-6_53

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 ().