Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Novel best path selection approach based on hybrid improved A* algorithm and reinforcement learning
X Liu, D Zhang, T Zhang, Y Cui, L Chen, S Liu - Applied Intelligence, 2021 - Springer
Path planning of intelligent driving vehicles in emergencies is a hot research issue, this
paper proposes a new method of the best path selection for the intelligent driving vehicles to …
paper proposes a new method of the best path selection for the intelligent driving vehicles to …
Intrusion response systems for cyber-physical systems: A comprehensive survey
Abstract Cyberattacks on Cyber-Physical Systems (CPS) are on the rise due to CPS
increased networked connectivity and may cause costly environmental hazards as well as …
increased networked connectivity and may cause costly environmental hazards as well as …
Zero knowledge clustering based adversarial mitigation in heterogeneous federated learning
The simultaneous development of deep learning techniques and Internet of Things
(IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for …
(IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for …
A survey for deep reinforcement learning in markovian cyber–physical systems: Common problems and solutions
Abstract Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical
systems for automation tasks. It is important to record the develo** trends in DRL's …
systems for automation tasks. It is important to record the develo** trends in DRL's …
Deep neural networks for spatial-temporal cyber-physical systems: A survey
Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …
computational elements into physical processes to facilitate the control of physical systems …
Toward Deep Q-Network-Based Resource Allocation in Industrial Internet of Things
With the increasing adoption of Industrial Internet-of-Things (IIoT) devices, infrastructures,
and supporting applications, it is critical to design schemes to effectively allocate resources …
and supporting applications, it is critical to design schemes to effectively allocate resources …
A review of techniques and policies on cybersecurity using artificial intelligence and reinforcement learning algorithms
Cybersecurity is a critical process that safeguards networks, systems, and applications
against cyber-attacks, wherein digital information is targeted for unauthorized access …
against cyber-attacks, wherein digital information is targeted for unauthorized access …
Reinforcement learning environment for cyber-resilient power distribution system
Recently, numerous data-driven approaches to control an electric grid using machine
learning techniques have been investigated. Reinforcement learning (RL)-based techniques …
learning techniques have been investigated. Reinforcement learning (RL)-based techniques …