The University of Southampton
University of Southampton Institutional Repository

Evaluating reinforcement learning for game theory application learning to price airline seats under competition

Collins, Andrew (2009) Evaluating reinforcement learning for game theory application learning to price airline seats under competition University of Southampton, School of Management, Doctoral Thesis , 266pp.

Record type: Thesis (Doctoral)


Applied Game Theory has been criticised for not being able to model real decision making situations. A game's sensitive nature and the difficultly in determining the utility payoff functions make it hard for a decision maker to rely upon any game theoretic results. Therefore the models tend to be simple due to the complexity of solving them (i.e. finding the equilibrium).
In recent years, due to the increases of computing power, different computer modelling techniques have been applied in Game Theory. A major example is Artificial Intelligence methods e.g. Genetic Algorithms, Neural Networks and Reinforcement Learning (RL). These techniques allow the modeller to incorporate Game Theory within their models (or simulation) without necessarily knowing the optimal solution. After a warm up period of repeated episodes is run, the model learns to play the game well (though not necessarily optimally). This is a form of simulation-optimization.
The objective of the research is to investigate the practical usage of RL within a simple sequential stochastic airline seat pricing game. Different forms of RL are considered and compared to the optimal policy, which is found using standard dynamic programming techniques. The airline game and RL methods displays various interesting phenomena, which are also discussed. For completeness, convergence proofs for the RL algorithms were constructed.

PDF Final_Phd_thesis_-_Andrew_Collins_January_2009.pdf - Other
Download (1MB)

More information

Published date: January 2009
Organisations: University of Southampton


Local EPrints ID: 69751
PURE UUID: 376427ff-4b80-4144-8513-afb4fa688ce2

Catalogue record

Date deposited: 03 Dec 2009
Last modified: 19 Jul 2017 00:06

Export record


Author: Andrew Collins
Thesis advisor: LYN C THOMAS

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.