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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 311–320 of 655 papers

TitleStatusHype
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Influencing Bandits: Arm Selection for Preference Shaping—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Information Directed Sampling and Bandits with Heteroscedastic Noise—0
Information Directed Sampling for Stochastic Bandits with Graph Feedback—0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
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