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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 341–350 of 655 papers

TitleStatusHype
Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization—0
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting—0
Exploration for Multi-task Reinforcement Learning with Deep Generative Models—0
Exploration via linearly perturbed loss minimisation—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
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