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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.

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Showing 351–360 of 655 papers

TitleStatusHype
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification—0
Fourier Representations for Black-Box Optimization over Categorical Variables—0
Freshness-Aware Thompson Sampling—0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Fully Distributed Bayesian Optimization with Stochastic Policies—0
Gaussian Process Thompson Sampling via Rootfinding—0
Generalized Bayesian deep reinforcement learning—0
Generalized Probabilistic Bisection for Stochastic Root-Finding—0
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors—0
Generalized Thompson Sampling for Contextual Bandits—0
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