Functional Natural Policy Gradients

Aurelien Bibaut; Houssam Zenati; Thibaud Rahier; Nathan Kallus

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arXiv · 2603.28681

Functional Natural Policy Gradients

Aurelien Bibaut·Houssam Zenati·Thibaud Rahier·Nathan Kallus

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Abstract

We propose a cross-fitted debiasing device for policy learning from offline data. A key consequence of the resulting learning principle is $\sqrt N$ regret even for policy classes with complexity greater than Donsker, provided a product-of-errors nuisance remainder is $O(N^{-1/2})$. The regret bound factors into a plug-in policy error factor governed by policy-class complexity and an environment nuisance factor governed by the complexity of the environment dynamics, making explicit how one may be traded against the other.

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Aurelien Bibaut, Houssam Zenati, Thibaud Rahier, Nathan Kallus. 2026-04-03. Functional Natural Policy Gradients. https://arxiv.org/abs/2603.28681

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