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.