Reinforcement Learning-Based Optimal Control for Multiplicative-Noise Systems with Input Delay
Abstract
In this paper, the reinforcement learning (RL)-based optimal control problem is studied for multiplicative-noise systems, where input delay is involved and partial system dynamics is unknown. To solve a variant of Riccati-ZXL equations, which is a counterpart of standard Riccati equation and determines the optimal controller, we first develop a necessary and sufficient stabilizing condition in form of several Lyapunov-type equations, a parallelism of the classical Lyapunov theory. Based on the condition, we provide an offline and convergent algorithm for the variant of Riccati-ZXL equations. According to the convergent algorithm, we propose a RL-based optimal control design approach for solving linear quadratic regulation problem with partially unknown system dynamics. Finally, a numerical example is used to evaluate the proposed algorithm.
Cite
@article{arxiv.2301.02812,
title = {Reinforcement Learning-Based Optimal Control for Multiplicative-Noise Systems with Input Delay},
author = {Hongxia Wang and Fuyu Zhao and Zhaorong Zhang and Juanjuan Xu and Xun Li},
journal= {arXiv preprint arXiv:2301.02812},
year = {2023}
}