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Algorithms
Volume 18
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10.3390/a18030170
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Article

Investigations into the Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks

by
Roxana-Elena Tudoroiu
1,
Mohammed Zaheeruddin
2,
Daniel-Ioan Curiac
3,
Mihai Sorin Radu
1 and
Nicolae Tudoroiu
4,*
1
Sciences and Electrical Engineering, University from Petrosani, 332006 Petrosani, Romania
2
Centre for Building Studies, Concordia University from Montreal, Montreal, QC H3G 1M8, Canada
3
Department of Automation and Applied Informatics, Polytechnic University of Timisoara, 300006 Timisoara, Romania
4
Engineering Technologies Department, John Abbott Collège in Sainte-Anne-de-Bellevue, Montréal, QC H9X 3L9, Canada
*
Author to whom correspondence should be addressed.
Algorithms 2025, 18(3), 170; https://doi.org/10.3390/a18030170
Submission received: 17 January 2025 / Revised: 26 February 2025 / Accepted: 13 March 2025 / Published: 16 March 2025
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))
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Abstract

This paper investigates the design and MATLAB/Simulink implementation of two intelligent neural reinforcement learning control algorithms based on deep learning neural network structures (RL DLNNs), for a complex Heating Ventilation Air Conditioning (HVAC) centrifugal chiller system (CCS). Our motivation to design such control strategies lies in this system’s significant control-related challenges, namely its high dimensionality and strongly nonlinear multi-input multi-output (MIMO) structure, coupled with strong constraints and a substantial impact of measured disturbance on tracking performance. As a beneficial vehicle for “proof of concept”, two simplified CCS MIMO models were derived, and an extensive number of simulations were run to demonstrate the effectiveness of both RL DLNN control algorithm implementations compared with two conventional control algorithms. The experiments involving the two investigated data-driven advanced neural control algorithms prove their high potential to adapt to various types of nonlinearities, singularities, dimensions, disruptions, constraints, and uncertainties that inherently characterize real-world processes.
Keywords: centrifugal chiller system; reinforcement learning; deep learning neural network centrifugal chiller system; reinforcement learning; deep learning neural network
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MDPI and ACS Style

Tudoroiu, R.-E.; Zaheeruddin, M.; Curiac, D.-I.; Radu, M.S.; Tudoroiu, N. Investigations into the Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks. Algorithms 2025, 18, 170. https://doi.org/10.3390/a18030170

AMA Style

Tudoroiu R-E, Zaheeruddin M, Curiac D-I, Radu MS, Tudoroiu N. Investigations into the Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks. Algorithms. 2025; 18(3):170. https://doi.org/10.3390/a18030170

Chicago/Turabian Style

Tudoroiu, Roxana-Elena, Mohammed Zaheeruddin, Daniel-Ioan Curiac, Mihai Sorin Radu, and Nicolae Tudoroiu. 2025. "Investigations into the Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks" Algorithms 18, no. 3: 170. https://doi.org/10.3390/a18030170

APA Style

Tudoroiu, R.-E., Zaheeruddin, M., Curiac, D.-I., Radu, M. S., & Tudoroiu, N. (2025). Investigations into the Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks. Algorithms, 18(3), 170. https://doi.org/10.3390/a18030170

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