The domain of Machine learning has experienced Substantial advancement and
development. Recently, showcasing a Broad spectrum of uses like Computational …

The heating, ventilating and air conditioning (HVAC) systems energy demand can be
reduced by manipulating indoor conditions within the comfort range, which relates to control …

In the field of Sequential Decision Making (SDM), two paradigms have historically vied for
supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of …

Uncovering the underlying causal structure of a phenomenon, domain or environment is of
great scientific interest, not least because of the inferences that can be derived from such …

Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful
behaviors in a task-agnostic environment without the guidance of extrinsic rewards to …

The interactions with humans, and simultaneously, making of real‐time decisions in physical
systems, are involved in many applications of artificial intelligence. An example of these …

Model Free Reinforcement Learning (MFRL) has shown significant promise for learning
dexterous robotic manipulation tasks, at least in simulation. However, the high number of …

This study provides an extensive review of over 200 journal papers focusing on Machine
Learning (ML) algorithms' use for promoting a sustainable management of the marine and …

Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the
central network, incorporating edge nodes close to end devices. This expansion facilitates …

Optimizing the cooling system plays a central role for capping the data center power
consumption. However, the performance of traditional cooling management strategies is not …