Degree Type

Thesis

Date of Award

2008

Degree Name

Master of Science

Department

Computer Science

First Advisor

Giora Slutzki

Second Advisor

Leigh Tesfatsion

Third Advisor

Vasant Honavar

Abstract

This work focuses on multi-agent learning in market contexts. It reports findings from a comparative study of three reinforcement learning algorithms currently in use for a variety of market applications. Two double-auction market testbeds are developed and used to carry out benchmark comparisons involving intensive parameter sweeps with heat map visualization of parameter sensitivities. A primary concern is the degree to which each tested algorithm permits learning agents to converge to the choice of a best action measured in terms of accumulated profits. Some findings from a mathematical analysis of the algorithms' properties are also reported.;The three reinforcement learning algorithms studied in this work are: the Roth-Erev algorithm proposed by Erev and Roth [1], the Modified Roth-Erev (MRE) reinforcement learning algorithm proposed by Nicolaisen et al. [2] and the Variant Roth-Erev (VRE) learning algorithm proposed by Sun and Tesfatsion [3].

DOI

https://doi.org/10.31274/rtd-180813-16062

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Mridul Pentapalli

Language

en

Proquest ID

AAI1453051

OCLC Number

235175797

ISBN

9780549540694

File Format

application/pdf

File Size

110 pages

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