Robust sequential decision-making on networks
Name
1136490586-MIT.pdf
Size
6.44 MB
Format
Adobe PDF
Checksum (MD5)
2f86030be6a61b2490b442eaf7430465
Author(s)
Dubey, Abhimanyu.
Advisor(s)
Alex P. Pentland.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I consider the research problem of designing optimal algorithms for two specific settings of the stochastic multi-armed bandit problem. The first setting considers the problem where rewards are drawn from a family of extremely heavy-tailed distributions known as a-stable distributions. For this setting, I extended an existing upper confidence bound algorithm, to create an optimal frequentist algorithm, titled [alpha]-UCB. Next, I developed a variant of the Bayesian Thompson Sampling algorithm in this setting, titled Robust [alpha]-TS, which involved developing an efficient pipeline for posterior inference. I also proved finite-time regret bounds for this algorithm, that are optimal up to logarithmic factors. The second problem setting I considered was the networked multi-agent problem where agents have local communication, and have unique preferences. This problem setting is a generalization of the co-operative multi-agent stochastic bandit problem, and is a closely related variant of the single-agent bandit setting with side observations. For this setting, I developed an optimal upper confidence bound algorithm, titled Net-UCB. I also proved finite-time regret bounds for this algorithm that are logarithmic in the number of rounds, and are sub-linear in the number of agents. For both settings, I conducted extensive experiments to verify the tightness of the regret bounds established, and compare performance with existing state-of-the-art algorithms. The algorithms proposed in this thesis obtain competitive regret and state-of-the-art performance across a variety of problem settings.
Description
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-106).
Subjects
Program in Media Arts and Sciences
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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