Guest Editorial: Special issue on neural networks-based reinforcement learning control of autonomous systems

Hamid Reza Karimi, Ning Wang, Xu Jin, Ali Zemouche

Research output: Contribution to journalEditorial

1 Scopus citations


Neural networks-based reinforcement learning control (NRLC) of autonomous systems is an active field due to its theoretical challenges and crucial applications. Note that there exist numerous difficulties in enhancing the intelligence and reliability of autonomous systems since autonomous and reliable techniques of guidance, navigation and control functionals are extremely involved in face of sophisticated and hazardous environments. In this context, high-intelligence reliable control technologies, especially based on neural networks tools, of autonomous systems are persistently pursued in trajectory tracking, path following, waypoints guidance, cooperative formation, etc. In addition, massive nonlinearities, sensor fault diagnosis, actuator failures tolerance, environment abnormalities, civil requirements and national security issues have led to strong demands for the NRLC technologies in autonomous systems. Reinforcement learning, inspired by learning mechanisms observed in mammals, is concerned with how agent and actor ought to take actions to optimize a cost of its long-term interactions with the environment, and is gradually becoming the focus of learning control for autonomous systems. The autonomous systems inevitably suffer from actuator faults, component failures, insecurity factors, complex uncertainties, such that neural networks induced intelligence in autonomous control, fault tolerant control, network communication and signal progressing becomes dramatically significant. To be specific, by combining with neural networks and reinforcement learning, advances in the NRLC technologies of autonomous systems are exclusively pursued in this special issue.

Original languageEnglish
Pages (from-to)226-228
Number of pages3
StatePublished - Jun 14 2022

Bibliographical note

Funding Information:
This work was partially supported by the Italian Ministry of Education, University and Research through the Project “Department of Excellence LIS4.0-Lightweight and Smart Structures for Industry 4.0”, the Liaoning Revitalization Talents Program (under Grant XLYC1807013), and the Equipment Pre-Research Fund of Key Laboratory (under Grant 6142215200106), NASA Kentucky under NASA award No: 80NSSC20M0047.

Publisher Copyright:
© 2021 Elsevier B.V.

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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