Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …

Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …

Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …

Over the last few years there has been rapid research growth into eXplainable Artificial
Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for …

The majority of multi-agent system implementations aim to optimise agents' policies with
respect to a single objective, despite the fact that many real-world problem domains are …

Wireless sensor networks (WSNs) have become one of the essential components of the
Internet of Things (IoT). In any IoT application, different sensor-based devices gather data …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

This paper studies the problem of offloading an application consisting of dependent tasks in
multi-access edge computing (MEC). This problem is challenging because multiple …