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Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem

Published on Aug 20, 20151618 Views

Junpei Komiyama

We study the $K$-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms. We introduce a tight asymp

COLT 2015 - Paris

Related categories

Active LearningComputational Learning TheoryOn-line LearningReinforcement LearningSemi-supervised Learning

Chapter list

regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem00:00
Motivation: perferencce elicitation00:08
Sushi - 100:19
Sushi - 200:25
Sushi - 300:31
Sushi - 400:38
Dueling bandit problem00:46
Condorcet assumption01:23
Main result 1: Regret lower bound01:57
Main result 2: The RMED algorithms02:27
Mai routine of RMD2FH03:04
Numerical Experiment03:40
Summary03:53