Functional Natural Policy Gradients

TL;DR

This paper introduces a new policy learning method from offline data that achieves near-optimal regret bounds by balancing policy complexity and environment dynamics.

Contribution

It proposes a cross-fitted debiasing device enabling $\

Findings

Achieves $\

findings

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.