Functional Natural Policy Gradients
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.
| Authors | Aurelien Bibaut, Houssam Zenati, Thibaud Rahier et al. |
|---|---|
| Primary category | stat.ML |
| Published | 30 March 2026 |
| Last updated at source | 3 April 2026 |
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