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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 591–600 of 655 papers

TitleStatusHype
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Context Attentive Bandits: Contextual Bandit with Restricted Context—0
Multi-dueling Bandits with Dependent Arms—0
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling—0
Mostly Exploration-Free Algorithms for Contextual BanditsCode0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Thompson Sampling for Linear-Quadratic Control Problems—0
Horde of Bandits using Gaussian Markov Random Fields—0
QoS-Aware Multi-Armed Bandits—0
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