Advances in Reinforcement Learning: A Comprehensive Review of Real-World Applications in Industry

Bibliographic Details
Parent link:	Acta Scientific Computer Sciences.— .— Hyderabad: Acta Scientific Vol. 5, iss. 5.— 2023.— P. 32-38
Main Author:	Usenko K. Kirill
Corporate Author:	National Research Tomsk Polytechnic University
Other Authors:	Goncharov V. I. Valery Ivanovich
Summary:	This paper investigates the current feasibility of utilizing reinforcement learning algorithms in the industrial sector. Although many studies have showcased the success of these algorithms in simulations or on isolated real-world objects, there is a paucity of research examining their wider implementation in real-world systems. In this study, we identify the obstacles that must be surmounted to fully leverage the potential benefits of reinforcement learning algorithms in practical applications. Moreover, we present a thorough overview of existing literature aimed at tackling these challenges. Текстовый файл
Published:	2023
Subjects:	Reinforcement Learning Deep Learning Sim-to-real Engineering Artificial Intelligence Control Robotics Autonomous Control труды учёных ТПУ электронный ресурс
Online Access:	https://actascientific.com/ASCS/ASCS-05-0441.php
Format:	Electronic Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=671038

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