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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 171–180 of 655 papers

TitleStatusHype
Approximate information for efficient exploration-exploitation strategies—0
Thompson Sampling under Bernoulli Rewards with Local Differential Privacy—0
Thompson sampling for improved exploration in GFlowNets—0
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits—0
Scalable Neural Contextual Bandit for Recommender Systems—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Incentivizing Exploration with Linear Contexts and Combinatorial Actions—0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
Combinatorial Neural Bandits—0
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