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 …

With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …

Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …

The past decade has seen a rapid penetration of electric vehicles (EVs) as more and more
logistics and transportation companies start to deploy electric vehicles (EVs) for service …

Extreme events are featured by high impact and low probability, which can cause severe
damage to power systems. There has been much research focused on resilience-driven …

Recently, there is an emerging trend to apply deep reinforcement learning to solve the
vehicle routing problem (VRP), where a learnt policy governs the selection of next node for …

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs).
Recent works have shown that attention-based RL models outperform recurrent neural …

Reinforcement learning (RL) has shown superior performance in solving sequential
decision problems. In recent years, RL is gradually being used to solve unmanned driving …

Deep reinforcement learning (DRL) has recently shown its success in tackling complex
combinatorial optimization problems. When these problems are extended to multiobjective …