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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 451–460 of 655 papers

TitleStatusHype
Non-Stationary Bandit Learning via Predictive Sampling—0
Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing—0
Non-Stationary Latent Bandits—0
No Regrets for Learning the Prior in Bandits—0
Observation-Free Attacks on Stochastic Bandits—0
On Adaptive Estimation for Dynamic Bernoulli Bandits—0
On Batch Bayesian Optimization—0
On Dynamic Pricing with Covariates—0
On Efficiency in Hierarchical Reinforcement Learning—0
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise—0
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