Recovering Robustness in Model-Free Reinforcement Learning

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 posing the (model-free) linear quadratic Gaussian (LQG) problem as a special instance of RL. A simple LQG example 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 increased robustness. Two simple examples are presented to demonstrate the proposed method for enhancing robustness during RL training.