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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 601–610 of 655 papers

TitleStatusHype
Stacked Thompson BanditsCode0
Thompson Sampling For Stochastic Bandits with Graph Feedback—0
Estimating Quality in Multi-Objective Bandits Optimization—0
Exploration for Multi-task Reinforcement Learning with Deep Generative Models—0
Nonparametric General Reinforcement Learning—0
Linear Thompson Sampling Revisited—0
Unimodal Thompson Sampling for Graph-Structured Arms—0
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits—0
A Formal Solution to the Grain of Truth Problem—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
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