Variance-Reduced Conservative Policy Iteration

Naman Agarwal; Brian Bullins; Karan Singh

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arXiv · 2212.06283

Variance-Reduced Conservative Policy Iteration

Naman Agarwal·Brian Bullins·Karan Singh

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Abstract

We study the sample complexity of reducing reinforcement learning to a sequence of empirical risk minimization problems over the policy space. Such reductions-based algorithms exhibit local convergence in the function space, as opposed to the parameter space for policy gradient algorithms, and thus are unaffected by the possibly non-linear or discontinuous parameterization of the policy class. We propose a variance-reduced variant of Conservative Policy Iteration that improves the sample complexity of producing a $\varepsilon$-functional local optimum from $O(\varepsilon^{-4})$ to $O(\varepsilon^{-3})$. Under state-coverage and policy-completeness assumptions, the algorithm enjoys $\varepsilon$-global optimality after sampling $O(\varepsilon^{-2})$ times, improving upon the previously established $O(\varepsilon^{-3})$ sample requirement.

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Naman Agarwal, Brian Bullins, Karan Singh. 2022-12-12. Variance-Reduced Conservative Policy Iteration. https://arxiv.org/abs/2212.06283

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