Reinforcement Q-Learning for PDF Tracking Control of Stochastic Systems with Unknown Dynamics

Weiqing Yang, Yuyang Zhou (), Yong Zhang () and Yan Ren
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Weiqing Yang: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Yuyang Zhou: School of Computing Engineering and Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Yong Zhang: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Yan Ren: School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

Mathematics, 2024, vol. 12, issue 16, 1-15

Abstract: Tracking control of the output probability density function presents significant challenges, particularly when dealing with unknown system models and multiplicative noise disturbances. To address these challenges, this paper introduces a novel tracking control algorithm based on reinforce-ment Q-learning. Initially, a B-spline model is employed to represent the original system, thereby transforming the control problem into a state weight tracking issue within the B-spline stochastic system model. Moreover, to tackle the challenge of unknown stochastic system dynamics and the presence of multiplicative noise, a model-free reinforcement Q-learning algorithm is employed to solve the control problem. Finally, the proposed algorithm’s effectiveness is validated through comprehensive simulation examples.

Keywords: tracking control; probability density function; reinforcement learing; B-spline model; Q-learning; model-free (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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