A Study on Cyclical Learning Rates in Reinforcement Learning and Its Application to Temperature and Power Consumption Control of Refrigeration System

Jingchen Wang, Kazuhiro Motegi (), Yoichi Shiraishi () and Seiji Hashimoto
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Jingchen Wang: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
Kazuhiro Motegi: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
Yoichi Shiraishi: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan
Seiji Hashimoto: Graduate School of Science and Technology, Gunma University, Kiryu 376-8515, Japan

Energies, 2024, vol. 17, issue 23, 1-13

Abstract: In recent years, with the advancement of computer hardware technology, an increasing number of complex control systems have begun employing reinforcement learning over traditional PID controls to address the challenge of managing multiple outputs simultaneously. In this study, we have for the first time adopted the cyclical learning rate method, which is widely used in deep learning, and applied it to deep reinforcement learning. Utilizing MATLAB Simulink, a detailed simulation model was developed with the RSD-4TFK5J model refrigeration storage as the reference object. We precisely evaluated the effects of various cyclical learning rate strategies on the training process of the model. The simulation results demonstrate the effectiveness of the cyclical learning rate method during the training phase, showcasing its potential to enhance learning efficiency and system performance in complex control environments.

Keywords: reinforcement learning; deep Q learning; refrigeration system; cyclical learning rate (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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