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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 211–220 of 655 papers

TitleStatusHype
Bag of Policies for Distributional Deep Exploration—0
Double Thompson Sampling in Finite stochastic Games—0
Online Multi-Armed Bandits with Adaptive Inference—0
Doubly robust Thompson sampling for linear payoffs—0
Doubly Robust Thompson Sampling with Linear Payoffs—0
DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN—0
Dual-Directed Algorithm Design for Efficient Pure Exploration—0
Bandit Convex Optimization: sqrtT Regret in One Dimension—0
Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
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