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Multiple-policy Evaluation via Density Estimation
April 2, 2024, 7:41 p.m. | Yilei Chen, Aldo Pacchiano, Ioannis Ch. Paschalidis
cs.LG updates on arXiv.org arxiv.org
Abstract: In this work, we focus on the multiple-policy evaluation problem where we are given a set of $K$ target policies and the goal is to evaluate their performance (the expected total rewards) to an accuracy $\epsilon$ with probability at least $1-\delta$. We propose an algorithm named $\mathrm{CAESAR}$ to address this problem. Our approach is based on computing an approximate optimal offline sampling distribution and using the data sampled from it to perform the simultaneous estimation …
abstract accuracy algorithm arxiv cs.ai cs.lg delta epsilon evaluation focus least multiple performance policies policy probability set total type via work
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