Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning

DOI10.1137/21M140924XOpenAlexW3202341388MaRDI QIDQ5089723

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Publication date: 15 July 2022

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2110.00871

zbMATH Keywords

reinforcement learningcontextual banditsThompson sampling

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• A decision-theoretic generalization of on-line learning and an application to boosting
• Information-theoretic determination of minimax rates of convergence
• The Nonstochastic Multiarmed Bandit Problem
• 10.1162/153244303321897663
• Competitive On-line Statistics
• Learning to Optimize via Posterior Sampling
• Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits Under Realizability

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