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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 391–400 of 655 papers

TitleStatusHype
Information Directed Sampling and Bandits with Heteroscedastic Noise—0
Information Directed Sampling for Stochastic Bandits with Graph Feedback—0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—0
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems—0
KLUCB Approach to Copeland Bandits—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Latent Bandits Revisited—0
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