Bregman Gradient Policy Optimization

TL;DR

This paper introduces a novel Bregman gradient policy optimization framework for reinforcement learning, providing algorithms with improved sample complexity and unifying existing methods, supported by theoretical analysis and experiments.

Contribution

It proposes a new Bregman gradient policy optimization framework with accelerated variants and convergence analysis, unifying and improving upon existing policy optimization algorithms.

Findings

BGPO achieves $O(psilon^{-4})$ sample complexity.

VR-BGPO achieves $O(psilon^{-3})$ sample complexity.

Experimental results demonstrate the efficiency of the proposed algorithms.

Abstract

In the paper, we design a novel Bregman gradient policy optimization framework for reinforcement learning based on Bregman divergences and momentum techniques. Specifically, we propose a Bregman gradient policy optimization (BGPO) algorithm based on the basic momentum technique and mirror descent iteration. Meanwhile, we further propose an accelerated Bregman gradient policy optimization (VR-BGPO) algorithm based on the variance reduced technique. Moreover, we provide a convergence analysis framework for our Bregman gradient policy optimization under the nonconvex setting. We prove that our BGPO achieves a sample complexity of $O(\epsilon^{-4})$ for finding $\epsilon$-stationary policy only requiring one trajectory at each iteration, and our VR-BGPO reaches the best known sample complexity of $O(\epsilon^{-3})$, which also only requires one trajectory at each iteration. In particular, by using different Bregman divergences, our BGPO framework unifies many existing policy optimization algorithms such as the existing (variance reduced) policy gradient algorithms such as natural policy gradient algorithm. Extensive experimental results on multiple reinforcement learning tasks demonstrate the efficiency of our new algorithms.