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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 101–110 of 655 papers

TitleStatusHype
DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN—0
Preferential Multi-Objective Bayesian Optimization—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—0
Memory Sequence Length of Data Sampling Impacts the Adaptation of Meta-Reinforcement Learning Agents—0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Graph Neural Thompson Sampling—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation—0
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