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arXiv Machine Learning By Hanbin Zhou, Shangzhe Li, Alexander Braverman, Weitong Zhang

Provable Benefits of Regularization: Fast Rates for Adversarial Imitation Learning

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The paper introduces Dually Regularized AIL, a model‑free algorithm for adversarial imitation learning that jointly applies KL policy regularization and a quadratic reward penalty based on expert and learner occupancies. It proves fast convergence rates, achieving a ×O(1/K+1/N) bound on the regularized imitation gap in finite‑horizon MDPs with general function approximation, and establishes the first algorithm to attain ×O(1/ε) sample complexity in both expert demonstrations and online interactions for this regularized objective.

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