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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 251–260 of 655 papers

TitleStatusHype
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs—0
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes—0
Thompson Sampling for (Combinatorial) Pure Exploration—0
Thompson Sampling Achieves O(T) Regret in Linear Quadratic Control—0
Thompson Sampling for Robust Transfer in Multi-Task BanditsCode0
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
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