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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 241–250 of 655 papers

TitleStatusHype
Thompson Sampling for Robust Transfer in Multi-Task BanditsCode0
Thompson Sampling Achieves O(T) Regret in Linear Quadratic Control—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
On Provably Robust Meta-Bayesian OptimizationCode0
Top Two Algorithms Revisited—0
Regret Bounds for Information-Directed Reinforcement Learning—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Incentivizing Combinatorial Bandit Exploration—0
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