The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications
Tamás Kegyes, Zoltán Süle, János Abonyi
Abstract
Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.
Topics & Concepts
Reinforcement learningComputer scienceReinforcementField (mathematics)Point (geometry)Development (topology)Artificial intelligenceManagement scienceIndustrial engineeringMachine learningMathematicsEngineeringMathematical analysisStructural engineeringGeometryPure mathematicsDigital Transformation in IndustryBlockchain Technology Applications and Security