Modern software and networks underpin our digital society, yet the rapid growth of
vulnerabilities that are uncovered within these threaten our cyber security posture …

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during
the last decade to solve various decision-making problems such as autonomous driving …

Deep Reinforcement Learning (DRL) has received a lot of attention from the research
community in recent years. As the technology moves away from game playing to practical …

Deep Reinforcement Learning (DRL) has made significant advancements in various fields,
such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal …

Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling
autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However …

Deep reinforcement learning (DRL) has demonstrated significant potential in industrial
manufacturing domains such as workshop scheduling and energy system management …

Learning-enabled controllers (LECs) pose severe challenges to verification. Their decisions
often come from deep neural networks that are hard to interpret and verify, and they operate …

The reliability of decision-making policies is urgently important today as they have
established the fundamentals of many critical applications, such as autonomous driving and …

The development process for reinforcement learning applications is still exploratory rather
than systematic. This exploratory nature reduces reuse of specifications between …

Techniques like deep reinforcement learning (DRL) enable autonomous agents to solve
tasks in complex environments automatically through learning. Despite their potential …