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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 461–470 of 655 papers

TitleStatusHype
Thompson Sampling for (Combinatorial) Pure Exploration—0
Thompson Sampling for Combinatorial Semi-Bandits—0
Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints—0
Thompson Sampling for Complex Bandit Problems—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling for Dynamic Pricing—0
Thompson Sampling for Gaussian Entropic Risk Bandits—0
Thompson sampling for improved exploration in GFlowNets—0
Thompson Sampling for Infinite-Horizon Discounted Decision Processes—0
Thompson Sampling for Learning Parameterized Markov Decision Processes—0
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