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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 641–650 of 655 papers

TitleStatusHype
Better Optimism By Bayes: Adaptive Planning with Rich Models—0
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search—0
Eluder Dimension and the Sample Complexity of Optimistic Exploration—0
Thompson Sampling for Complex Bandit Problems—0
Thompson Sampling for Online Learning with Linear Experts—0
Generalized Thompson Sampling for Contextual Bandits—0
Thompson Sampling in Dynamic Systems for Contextual Bandit Problems—0
Thompson Sampling for 1-Dimensional Exponential Family Bandits—0
Cover Tree Bayesian Reinforcement Learning—0
Prior-free and prior-dependent regret bounds for Thompson Sampling—0
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