Electrified vehicles provide an effective solution to address the unfavorable impacts of fossil
fuel use in the transportation sector. Energy management strategy (EMS) is the core …

Utility grids are undergoing several upgrades. Distributed generators that are supplied by
intermittent renewable energy sources (RES) are being connected to the grids. As RES get …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

Tracking maneuvering target in real time autonomously and accurately in an uncertain
environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this …

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence
(AI) by endowing autonomous systems with high levels of understanding of the real world …

Reinforcement learning has recently been studied in various fields and also used to
optimally control IoT devices supporting the expansion of Internet connection beyond the …

Evolutionary computation has largely exhibited its potential to complement conventional
learning algorithms in a variety of machine learning tasks, especially those related to …

Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis.
Late diagnosis is common due to a lack of early symptoms, specific markers, and the …

Autonomously learning a complex task takes a very long time for Reinforcement Learning
(RL) agents. One way to learn faster is by dividing a complex task into several simple …