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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 391–400 of 655 papers

TitleStatusHype
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Reinforcement Learning with Subspaces using Free Energy Paradigm—0
Reinforcement Learning with Trajectory Feedback—0
Remote Contextual Bandits—0
Residual Bootstrap Exploration for Bandit Algorithms—0
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Risk and optimal policies in bandit experiments—0
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs—0
Risk-Constrained Thompson Sampling for CVaR Bandits—0
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