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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 371–380 of 655 papers

TitleStatusHype
Etat de l'art sur l'application des bandits multi-bras—0
Meta-Reinforcement Learning With Informed Policy Regularization—0
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 Arrivals—0
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Distributed Thompson Sampling—0
On Efficiency in Hierarchical Reinforcement Learning—0
Non-Stationary Latent Bandits—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Risk-Constrained Thompson Sampling for CVaR Bandits—0
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