Variance Reduction based Experience Replay for Policy Optimization

TL;DR

This paper introduces a variance reduction experience replay framework that selectively reuses relevant past samples to improve policy gradient estimates, accelerating reinforcement learning in complex stochastic systems.

Contribution

The paper proposes a novel VRER method that adaptively prioritizes samples for more efficient policy optimization, outperforming uniform replay strategies.

Findings

VRER accelerates policy learning.

VRER improves policy performance.

Theoretical and empirical validation of VRER.

Abstract

For reinforcement learning on complex stochastic systems where many factors dynamically impact the output trajectories, it is desirable to effectively leverage the information from historical samples collected in previous iterations to accelerate policy optimization. Classical experience replay allows agents to remember by reusing historical observations. However, the uniform reuse strategy that treats all observations equally overlooks the relative importance of different samples. To overcome this limitation, we propose a general variance reduction based experience replay (VRER) framework that can selectively reuse the most relevant samples to improve policy gradient estimation. This selective mechanism can adaptively put more weight on past samples that are more likely to be generated by the current target distribution. Our theoretical and empirical studies show that the proposed VRER can accelerate the learning of optimal policy and enhance the performance of state-of-the-art policy optimization approaches.