Multi-armed bandits and applications to large datasets
Kong, Seo Taek
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https://hdl.handle.net/2142/105019
Description
Title
Multi-armed bandits and applications to large datasets
Author(s)
Kong, Seo Taek
Issue Date
2019-04-15
Director of Research (if dissertation) or Advisor (if thesis)
Srikant, Rayadurgam
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Multi-Armed Bandits, Boltzmann Exploration
Abstract
This thesis considers the multi-armed bandit (MAB) problem, both the traditional bandit feedback and graphical bandits when there is side information. Motivated by the Boltzmann exploration algorithm often used in the more general context of reinforcement learning, we present Almost Boltzmann Exploration (ABE) which fixes the under-exploration issue while maintaining an expression similar to Boltzmann exploration. We then present some real world applications of the MAB framework, comparing the performance of ABE with other bandit algorithms on real world datasets.
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