Variance-Reduced Policy Gradient is Nearly Minimax-Optimal

Abstract:Standard Policy Gradient (PG) algorithms suffer from high estimation variance because they rely on Monte Carlo updates. While off-policy variance reduction mitigates this problem, establishing optimal sample complexity currently requires either restrictive assumptions, such as uniformly bounded importance weights, or impractical second-order operations. In this work, we introduce Loopless Variance-Reduced PG (LVR-PG), a unified theoretical framework that abstracts a class of first-order estimators that integrate recursive updates with defensive importance sampling, including known estimators such as PAGE-PG and STORM-PG. We prove that this algorithmic class achieves $\tilde{\mathcal{O}}(\epsilon^{-2})$ last-iterate global convergence under weak gradient domination for general parametric policies. We establish this guarantee using purely first-order updates, with minimal assumptions and without gradient preconditioners. By extending state-of-the-art lower bounds to continuous parametric policies, we establish an $\Omega(\epsilon^{-2})$ sample complexity limit, confirming our framework is minimax-optimal up to logarithmic factors and horizon terms.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.13692 [cs.LG]

(or arXiv:2610.13692v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.13692 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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