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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.

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Showing 471–480 of 655 papers

TitleStatusHype
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors—0
Thompson Sampling for Linear-Quadratic Control Problems—0
Thompson sampling for linear quadratic mean-field teams—0
Thompson Sampling for Noncompliant Bandits—0
Thompson Sampling for Online Learning with Linear Experts—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
Thompson Sampling for Pursuit-Evasion Problems—0
Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit—0
Thompson Sampling For Stochastic Bandits with Graph Feedback—0
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis—0
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