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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ammar, H.B., Taylor, M.E.: Reinforcement learning transfer via common subspaces. In: Vrancx, P., Knudson, M., Grześ, M. (eds.) ALA 2011. LNCS (LNAI), vol. 7113, pp. 21–36. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28499-1_2
Goodfellow, I.J., Bengio, Y., Courville, A.C.: Deep Learning. Adaptive Computation and Machine Learning, MIT Press, Cambridge (2016)
Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: ICML 2018, 10-15, 2018. Proceedings of Machine Learning Research, vol. 80, pp. 1856–1865. PMLR (2018)
Harsha, P., Subramanian, S., Uichanco, J.: Dynamic pricing of omnichannel inventories. Manuf. Serv. Oper. Manag. 21(1), 47–65 (2019)
Kastius, A., Schlosser, R.: Dynamic pricing under competition using reinforcement learning. J. Revenue Pricing Manag. 21, 50–63 (2022)
Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction. Adaptive Computation and Machine Learning, MIT Press, Cambridge (1998)
Teh, Y.W., et al.: Distral: robust multitask reinforcement learning. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp. 4496–4506 (2017)
Zhu, Z., Lin, K., Zhou, J.: Transfer learning in deep reinforcement learning: a survey. CoRR abs/2009.07888 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kastius, A., Schlosser, R. (2022). Towards Transfer Learning for Revenue and Pricing Management. In: Trautmann, N., Gnägi, M. (eds) Operations Research Proceedings 2021. OR 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-08623-6_53
Download citation
DOI: https://doi.org/10.1007/978-3-031-08623-6_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08622-9
Online ISBN: 978-3-031-08623-6
eBook Packages: Business and ManagementBusiness and Management (R0)