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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 181–190 of 655 papers

TitleStatusHype
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation—0
Discounted Thompson Sampling for Non-Stationary Bandit Problems—0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
Trajectory-oriented optimization of stochastic epidemiological modelsCode0
An improved regret analysis for UCB-N and TS-N—0
Neural Exploitation and Exploration of Contextual BanditsCode1
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded RewardsCode0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
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