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arXiv · 2003.04302

Stochastic Recursive Momentum for Policy Gradient Methods

Huizhuo Yuan·Xiangru Lian·Ji Liu·Yuren Zhou
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Abstract

In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced policy gradient in an exponential moving average fashion. STORM-PG enjoys a provably sharp $O(1/\epsilon^3)$ sample complexity bound for STORM-PG, matching the best-known convergence rate for policy gradient algorithm. In the mean time, STORM-PG avoids the alternations between large batches and small batches which persists in comparable variance-reduced policy gradient methods, allowing considerably simpler parameter tuning. Numerical experiments depicts the superiority of our algorithm over comparative policy gradient algorithms.

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Huizhuo Yuan, Xiangru Lian, Ji Liu, Yuren Zhou. 2020-03-09. Stochastic Recursive Momentum for Policy Gradient Methods. https://arxiv.org/abs/2003.04302

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