A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution
AH Ganesh, B Xu - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
Resilient machine learning for networked cyber physical systems: A survey for machine learning security to securing machine learning for CPS
Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
A survey on network security for cyber–physical systems: From threats to resilient design
Cyber-physical systems (CPS) are considered the integration of physical systems in the real
world and control software in computing systems. In CPS, the real world and the computing …
world and control software in computing systems. In CPS, the real world and the computing …
Deep reinforcement learning for autonomous internet of things: Model, applications and challenges
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …
around the world, where the IoT devices collect and share information to reflect status of the …
Deep-reinforcement-learning-based mode selection and resource allocation for cellular V2X communications
Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse
vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle …
vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle …
Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G
Employing machine learning into 6G vehicular networks to support vehicular application
services is being widely studied and a hot topic for the latest research works in the literature …
services is being widely studied and a hot topic for the latest research works in the literature …
Intelligent resource management based on reinforcement learning for ultra-reliable and low-latency IoV communication networks
Internet of Vehicles (IoV) has attracted much interest recently due to its ubiquitous message
exchange and content sharing among smart vehicles with the development of the mobile …
exchange and content sharing among smart vehicles with the development of the mobile …
Deep learning based caching for self-driving cars in multi-access edge computing
Without steering wheel and driver's seat, the self-driving cars will have new interior outlook
and spaces that can be used for enhanced infotainment services. For traveling people, self …
and spaces that can be used for enhanced infotainment services. For traveling people, self …
Meta-reinforcement learning based resource allocation for dynamic V2X communications
This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I)
and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing …
and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing …