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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 411–420 of 655 papers

TitleStatusHype
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits—0
Linear Bandit algorithms using the Bootstrap—0
Linear Thompson Sampling Revisited—0
Little Exploration is All You Need—0
Maillard Sampling: Boltzmann Exploration Done Optimally—0
Making RL with Preference-based Feedback Efficient via Randomization—0
Making Sense of Reinforcement Learning and Probabilistic Inference—0
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow—0
Optimization-Driven Adaptive Experimentation—0
Memory Sequence Length of Data Sampling Impacts the Adaptation of Meta-Reinforcement Learning Agents—0
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