Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

There has been a paradigm-shift in urban logistic services in the last years; demand for real-
time, instant mobility and delivery services grows. This poses new challenges to logistic …

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as
it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic …

This paper presents an end-to-end deep reinforcement learning (DRL) approach aimed at
efficiently determining energy-optimal routes for a group of electric logistic vehicles, with the …

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial
optimisation problems for which numerous models and algorithms have been proposed. To …