arXiv cs.LG
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Learning in Markovian bandits with non-observable states and constrained decision epochs
arXiv:2606.27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with emph{non-observable states} and possibly emph{constrained} decision epochs. The focus is restricted to a ``pure'' regret benchmark, that compares the performance of the learning algorithm to