AIBullisharXiv – CS AI · Feb 277/109
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Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
Researchers achieved breakthrough sample complexity improvements for offline reinforcement learning algorithms using f-divergence regularization, particularly for contextual bandits. The study demonstrates optimal O(ε⁻¹) sample complexity under single-policy concentrability conditions, significantly improving upon existing bounds.
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