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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

Showing 361370 of 655 papers

TitleStatusHype
TSEC: a framework for online experimentation under experimental constraints0
Deciding What to Learn: A Rate-Distortion Approach0
Etat de l'art sur l'application des bandits multi-bras0
Meta-Reinforcement Learning With Informed Policy Regularization0
Learning to Play Imperfect-Information Games by Imitating an Oracle PlannerCode0
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals0
Mercer Features for Efficient Combinatorial Bayesian OptimizationCode1
Reinforcement Learning with Subspaces using Free Energy Paradigm0
Optimal Thompson Sampling strategies for support-aware CVaR banditsCode1
Distributed Thompson Sampling0
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