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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 131–140 of 655 papers

TitleStatusHype
Blind Exploration and Exploitation of Stochastic Experts—0
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing—0
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles—0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Belief Flows of Robust Online Learning—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Best Arm Identification in Batched Multi-armed Bandit Problems—0
Bayesian Quantile and Expectile Optimisation—0
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