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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 621–630 of 655 papers

TitleStatusHype
Thompson Sampling is Asymptotically Optimal in General Environments—0
Convolutional Monte Carlo Rollouts in Go—0
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
TSEB: More Efficient Thompson Sampling for Policy Learning—0
Incentivizing Exploration In Reinforcement Learning With Deep Predictive ModelsCode0
Bootstrapped Thompson Sampling and Deep Exploration—0
On the Prior Sensitivity of Thompson Sampling—0
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple PlaysCode0
Belief Flows of Robust Online Learning—0
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