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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 271–280 of 655 papers

TitleStatusHype
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Context Attentive Bandits: Contextual Bandit with Restricted Context—0
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems—0
Constrained Thompson Sampling for Wireless Link Optimization—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints—0
Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection—0
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach—0
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio—0
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