Sample Efficient Policy Gradient Methods with Recursive Variance Reduction

arXiv1909.08610MaRDI QIDQ6325577

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Publication date: 18 September 2019

Abstract: Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires $O(1/epsilo{n}^{3/2})$ episodes to find an $epsilon$-approximate stationary point of the nonconcave performance function $$ (i.e., $$ such that $$). This sample complexity improves the existing result $O(1/epsilo{n}^{5/3})$ for stochastic variance reduced policy gradient algorithms by a factor of $O(1/epsilo{n}^{1/6})$. In addition, we also propose a variant of SRVR-PG with parameter exploration, which explores the initial policy parameter from a prior probability distribution. We conduct numerical experiments on classic control problems in reinforcement learning to validate the performance of our proposed algorithms.

Has companion code repository: https://github.com/xgfelicia/SRVRPG

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