The paper establishes the first tilde O(epsilon^{-1}) upper bounds and matching lower bounds for forward-KL-regularized offline contextual bandits under single-policy concentrability in both tabular and general function approximation settings.
Heyang Zhao, Chenlu Ye, Quanquan Gu, and Tong Zhang
2 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
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Optimistic regression algorithms with Gibbs updates achieve high-probability KL-regret that degrades gracefully under pointwise KL misspecification for bandits and stagewise KL Bellman misspecification for episodic RL.
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Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
The paper establishes the first tilde O(epsilon^{-1}) upper bounds and matching lower bounds for forward-KL-regularized offline contextual bandits under single-policy concentrability in both tabular and general function approximation settings.
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Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
Optimistic regression algorithms with Gibbs updates achieve high-probability KL-regret that degrades gracefully under pointwise KL misspecification for bandits and stagewise KL Bellman misspecification for episodic RL.