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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 281–290 of 655 papers

TitleStatusHype
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
Accelerating Grasp Exploration by Leveraging Learned Priors—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
Combining Bayesian Optimization and Lipschitz Optimization—0
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees—0
Combinatorial Neural Bandits—0
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret—0
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