Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning

Rupal Rana and Fernando S. Oliveira

Omega, 2014, vol. 47, issue C, 116-126

Abstract: This paper examines the problem of establishing a pricing policy that maximizes the revenue for selling a given inventory by a fixed deadline. This problem is faced by a variety of industries, including airlines, hotels and fashion. Reinforcement learning algorithms are used to analyze how firms can both learn and optimize their pricing strategies while interacting with their customers. We show that by using reinforcement learning we can model the problem with inter-dependent demands. This type of model can be useful in producing a more accurate pricing scheme of services or products when important events affect consumer preferences. This paper proposes a methodology to optimize revenue in a model-free environment in which demand is learned and pricing decisions are updated in real-time. We compare the performance of the learning algorithms using Monte-Carlo simulation.

Keywords: Revenue management; Dynamic pricing; Reinforcement learning; Simulation (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.omega.2013.10.004

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