Comparing reinforcement learning approaches for solving game theoretic models: a dynamic airline pricing game example


Collins, A.J. and Thomas, Lyn C. (2011) Comparing reinforcement learning approaches for solving game theoretic models: a dynamic airline pricing game example Journal of the Operational Research Society, 63, pp. 1165-1173. (doi:10.1057/jors.2011.94).

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Description/Abstract

Due to the difficulty in solving game theoretic models, there is a tendency to focus on the overly simplistic dynamic airline pricing games or to even ignore competition completely. Recent changes to the industry mean that airlines can no longer ignore competitors in their model. This paper adds more complex customer model aspects; i.e., customer choice using a Logit model, customer demand using a linear probabilistic demand model, and market size using a binary random function; into an existing solvable game that only had a simple customer model. A reinforcement learning method was used to solve the newly formed games with mixed results.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1057/jors.2011.94
ISSNs: 0160-5682 (print)
Keywords: game theory, artificial intelligence, reinforcement learning, air transport
Subjects:
Organisations: Centre of Excellence for International Banking, Finance & Accounting
ePrint ID: 343123
Date :
Date Event
December 2011e-pub ahead of print
August 2012Published
Date Deposited: 24 Sep 2012 15:19
Last Modified: 17 Apr 2017 16:36
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/343123

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