Recovering Robustness in Model-Free Reinforcement learning

TL;DR

This paper investigates the robustness of model-free reinforcement learning controllers, demonstrating that introducing input perturbations during training can improve robustness at the expense of some performance.

Contribution

It proposes a novel method of adding random input perturbations during RL training to enhance controller robustness, especially in linear quadratic Gaussian settings.

Findings

Perturbed RL training improves robustness margins.

Trade-off between robustness and performance is controllable.

Simple examples validate the robustness enhancement approach.

Abstract

Reinforcement learning (RL) is used to directly design a control policy using data collected from the system. This paper considers the robustness of controllers trained via model-free RL. The discussion focuses on the standard model-based linear quadratic Gaussian (LQG) problem as a special instance of RL. A simple example, originally formulated for LQG problems, is used to demonstrate that RL with partial observations can lead to poor robustness margins. It is proposed to recover robustness by introducing random perturbations at the system input during the RL training. The perturbation magnitude can be used to trade off performance for robustness. Two simple examples are presented to demonstrate the proposed method for enhancing robustness during RL training.