Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region Approach

TL;DR

This paper introduces a new reinforcement learning method that enhances policy stability and robustness in data-scarce scenarios by explicitly managing uncertainty during training.

Contribution

It proposes Uncertainty-Aware Trust Region Policy Optimization, a novel approach that adapts policy updates based on uncertainty estimates to improve stability with limited data.

Findings

Achieves stable policy learning with small sample sizes.

Provides robustness against high-dimensional estimate errors.

Outperforms traditional methods in data-limited settings.

Abstract

In order for reinforcement learning techniques to be useful in real-world decision making processes, they must be able to produce robust performance from limited data. Deep policy optimization methods have achieved impressive results on complex tasks, but their real-world adoption remains limited because they often require significant amounts of data to succeed. When combined with small sample sizes, these methods can result in unstable learning due to their reliance on high-dimensional sample-based estimates. In this work, we develop techniques to control the uncertainty introduced by these estimates. We leverage these techniques to propose a deep policy optimization approach designed to produce stable performance even when data is scarce. The resulting algorithm, Uncertainty-Aware Trust Region Policy Optimization, generates robust policy updates that adapt to the level of uncertainty present throughout the learning process.