An asymptotically optimal policy for finite support models in the multiarmed bandit problem

DOI10.1007/s10994-011-5257-4zbMath1237.91037arXiv0905.2776OpenAlexW2131958277WikidataQ56675674 ScholiaQ56675674MaRDI QIDQ415624

Junya Honda, Akimichi Takemura

Publication date: 8 May 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0905.2776

zbMATH Keywords

convex optimizationbandit problemsfinite-time regretMED policy

Mathematics Subject Classification ID

Convex programming (90C25) Stochastic games, stochastic differential games (91A15) Rationality and learning in game theory (91A26)

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• The Continuum-Armed Bandit Problem
• Elements of Information Theory
• Some aspects of the sequential design of experiments
• Convergence of stochastic processes
• Finite-time analysis of the multiarmed bandit problem

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