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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
Non-Stationary Latent Bandits—0
On Efficiency in Hierarchical Reinforcement Learning—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning—0
Risk-Constrained Thompson Sampling for CVaR Bandits—0
Accelerating Grasp Exploration by Leveraging Learned Priors—0
Thompson sampling for linear quadratic mean-field teams—0
Multi-Agent Active Search using Realistic Depth-Aware Noise ModelCode0
Asymptotic Convergence of Thompson Sampling—0
Adaptive Combinatorial Allocation—0
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