Gökhan DemirkıranOzcan ErdenerOnay AkpinarPelin DemirtasM. Yagiz ArikEmre GulerAkpinar, OnayGuler, EmreDemirkiran, GokhanDemirtas, PelinErdener, OzcanArik, M. Yagiz2025-10-062020978172819136210.1109/ASYU50717.2020.92598902-s2.0-85097962351https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097962351&doi=10.1109%2FASYU50717.2020.9259890&partnerID=40&md5=166efe3848f6eafce9cdcc2d243abf73https://gcris.yasar.edu.tr/handle/123456789/9149https://doi.org/10.1109/ASYU50717.2020.9259890The aim of this study is to implement Q-learning algorithm to move an inverted pendulum from the downright position to upright position in a PLC environment. Instead of using classical control algorithms that need a linear model of the system to be controlled we used model-free control algorithm i.e. Q-learning and relaxed the linearity assumption. We demonstrate that reinforcement learning can be successfully used in industrial machine learning applications to learn complex control policies without having a detailed model of the controlled system. An experimental set up is designed using PLC controlled mechanical parts and the code is written in PLC. After about three hours of learning stage the Q learning algorithm successfully moved inverted pendulum from downright position to upright position and keep it in balanced upright position. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessInverted Pendulum, Programmable Logic Controller, Reinforcement Learning, Intelligent Systems, Learning Systems, Pendulums, Reinforcement Learning, Controlled System, Detailed Modeling, Experimental Set Up, Industrial Machines, Inverted Pendulum, Model-free Control, Q-learning Algorithms, Reinforcement Learning Method, Learning AlgorithmsIntelligent systems, Learning systems, Pendulums, Reinforcement learning, Controlled system, Detailed modeling, Experimental set up, Industrial machines, Inverted pendulum, Model-free control, Q-learning algorithms, Reinforcement learning method, Learning algorithmsProgrammable Logic ControllerInverted PendulumReinforcement LearningConference Object