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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
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow—0
Random Effect Bandits—0
Thompson Sampling for Unimodal Bandits—0
Thompson Sampling with a Mixture Prior—0
Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?—0
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms—0
Parallelizing Thompson Sampling—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Asymptotically Optimal Bandits under Weighted Information—0
Diffusion Approximations for Thompson Sampling—0
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