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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 411–420 of 655 papers

TitleStatusHype
Reinforcement Learning with Trajectory Feedback—0
Lenient Regret for Multi-Armed Bandits—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Greedy Bandits with Sampled Context—0
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems—0
Variable Selection via Thompson Sampling—0
Policy Gradient Optimization of Thompson Sampling Policies—0
Asynchronous Multi Agent Active Search—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints—0
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