Fast Rates for Offline Contextual Bandits with Forward-KL Regularization
Researchers from the machine learning community have published a new study addressing statistical analysis challenges in reinforcement learning, specifically focusing on offline contextual bandits with forward Kullback-Leibler (KL) regularization. While reverse KL regularization has previously demonstrated fast convergence rates, forward KL objectives were limited to slower standard rates. This paper introduces the first streamlined analysis achieving fast O(epsilon^-1) upper bounds for forward-KL-regularized offline contextual bandits in both tabular and general function approximation settings. The authors employ a novel convex-analytical pipeline that leverages the pessimism principle, bypassing traditional proof routines based on the mean value theorem. Furthermore, the study provides rate-optimal lower bounds, confirming the tightness of their upper bounds. The findings indicate that forward-KL-regularized sample complexity recovers unregularized slow rates in low-regularization regimes, similar to reverse KL. This work represents a significant theoretical advancement in understanding sample complexity and optimization strategies within modern reinforcement learning algorithms.
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Fast Rates for Offline Contextual Bandits with Forward-KL Regularization
Researchers from the machine learning community have published a new study addressing statistical analysis challenges in reinforcement learning, specifically focusing on offline contextual bandits with forward Kullback-Leibler (KL) regularization. While reverse KL regularization has previously demonstrated fast convergence rates, forward KL objectives were limited to slower standard rates. This paper introduces the first streamlined analysis achieving fast O(epsilon^-1) upper bounds for forward-KL-regularized offline contextual bandits in both tabular and general function approximation settings. The authors employ a novel convex-analytical pipeline that leverages the pessimism principle, bypassing traditional proof routines based on the mean value theorem. Furthermore, the study provides rate-optimal lower bounds, confirming the tightness of their upper bounds. The findings indicate that forward-KL-regularized sample complexity recovers unregularized slow rates in low-regularization regimes, similar to reverse KL. This work represents a significant theoretical advancement in understanding sample complexity and optimization strategies within modern reinforcement learning algorithms.
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