Recovering Robustness in Model-Free Reinforcement learning
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.
Cite
@article{arxiv.1810.09337,
title = {Recovering Robustness in Model-Free Reinforcement learning},
author = {Harish K. Venkataraman and Peter J. Seiler},
journal= {arXiv preprint arXiv:1810.09337},
year = {2019}
}
Comments
Github Code Repository: https://github.com/kumaa001/RLRobustness (Note : The files have been named to match with the section names and number. The comments in the code explains the procedure step by step. The data from the .mat file could be pulled into the work-space to avoid the need for complete code execution)