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Multiagent learning and empirical methods

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dc.contributor.author Zawadzki, Erik P.
dc.date.accessioned 2008-10-06T20:43:57Z
dc.date.available 2008-10-06T20:43:57Z
dc.date.copyright 2008 en
dc.date.issued 2008-10-06T20:43:57Z
dc.identifier.uri http://hdl.handle.net/2429/2480
dc.description.abstract Many algorithms exist for learning how to act in a repeated game and most have theoretical guarantees associated with their behaviour. However, there are few experimental results about the empirical performance of these algorithms, which is important for any practical application of this work. Most of the empirical claims in the literature to date have been based on small experiments, and this has hampered the development of multiagent learning (MAL) algorithms with good performance properties. In order to rectify this problem, we have developed a suite of tools for running multiagent experiments called the Multiagent Learning Testbed (MALT). These tools are designed to facilitate running larger and more comprehensive experiments by removing the need to code one-off experimental apparatus. MALT also provides a number of public implementations of MAL algorithms—hopefully eliminating or reducing differences between algorithm implementations and increasing the reproducibility of results. Using this test-suite, we ran an experiment that is unprecedented in terms of the number of MAL algorithms used and the number of game instances generated. The results of this experiment were analyzed by using a variety of performance metrics—including reward, maxmin distance, regret, and several types of convergence. Our investigation also draws upon a number of empirical analysis methods. Through this analysis we found some surprising results: the most surprising observation was that a very simple algorithm—one that was intended for single-agent reinforcement problems and not multiagent learning— performed better empirically than more complicated and recent MAL algorithms. en
dc.format.extent 857498 bytes
dc.format.mimetype application/pdf
dc.language.iso eng en
dc.publisher University of British Columbia
dc.relation.ispartof Electronic Theses and Dissertations (ETDs) 2008+ en
dc.subject MAL en
dc.title Multiagent learning and empirical methods en
dc.type Text
dc.degree.name Master of Science - MSc en
dc.degree.discipline Computer Science en
dc.degree.grantor University of British Columbia
dc.date.graduation 2008-11 en
dc.type.text Thesis/Dissertation
dc.description.affiliation Science, Faculty of
dc.degree.campus UBCV en
dc.description.scholarlevel Graduate en

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