Momentum-Based Policy Gradient with Second-Order Information

TL;DR

This paper introduces SHARP, a variance-reduced policy gradient method that leverages second-order information and momentum, achieving efficient convergence without importance sampling and demonstrating superior performance in control tasks.

Contribution

The paper proposes SHARP, a novel variance-reduced policy gradient algorithm that incorporates second-order information and momentum, with theoretical guarantees and practical advantages over existing methods.

Findings

Achieves $ ext{O}( ext{}\epsilon^{-3})$ sample complexity for stationary points.

Does not require importance sampling, simplifying implementation.

Outperforms state-of-the-art methods in control task experiments.

Abstract

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient method, called SHARP, which incorporates second-order information into stochastic gradient descent (SGD) using momentum with a time-varying learning rate. SHARP algorithm is parameter-free, achieving $\epsilon$-approximate first-order stationary point with $O(\epsilon^{-3})$ number of trajectories, while using a batch size of $O(1)$ at each iteration. Unlike most previous work, our proposed algorithm does not require importance sampling which can compromise the advantage of variance reduction process. Moreover, the variance of estimation error decays with the fast rate of $O(1/t^{2/3})$ where $t$ is the number of iterations. Our extensive experimental evaluations show the effectiveness of the proposed algorithm on various control tasks and its advantage over the state of the art in practice.