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

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.