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Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits

Spyros Dragazis et al.
arXiv CS · Papers
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deep-learningmachine-learningneural-networks
We study how to evaluate $K$ target policies simultaneously in stochastic contextual bandits with linear reward structure. Given a known $d$-dimensional feature map and reward-free access to policy queries, we propose PRISMA (Policy evaluation through Regularized Information Sharing and Mixture Allo
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