Numerical parameter continuation methods are popularly utilized to optimize non-convex
problems. These methods have had many applications in Physics and Mathematical …

Increasing delays and congestion reported in many aviation sectors indicate that the current
centralised operational model is rapidly approaching saturation levels. Air Traffic Control …

Deep neural networks (DNNs) have been established as a powerful tool to solve a wide
variety of real-world problems whose complexities had previously been considered too …

The field of machine learning (ML) has achieved remarkable success over the past few
decades and has become an integral part of our daily lives, powering various applications …

This research aimed to investigate the synergies between deep learning and
heterogeneous graph-based scenario modeling. The candidate has thoroughly studied the …

Earlier works have reported that reward shaping accelerates the convergence of
reinforcement learning algorithms. It also helps to make better use of existing information. In …

This chapter introduces the topic of this dissertation and provides an overview of it. Section 1
introduces the challenge of reinforcement learning and our purpose and solutions, and …

Consider the issue of optimising the overall travel times for multiple trains running
simultaneously in a railway network. In this scenario, we would only be able to control the …

To drive a vehicle fully autonomously, an intelligent system needs to be capable of having
accurate perception and comprehensive understanding of the surroundings, making …

Reinforcement Learning (RL) is a subfield of Machine Learning (ML) focuses on how agents
learn to make optimal decisions over time to achieve particular goals. Given its emphasis on …