Multi-agent area coverage aims to accomplish the complete traversal of the target area
through cooperation between agents. Focusing on the problems of low coverage efficiency …

Reinforcement learning approach has been preferred by researchers and scientists in
recent years, especially due to its superior performance in robot studies. While smart …

This paper studies the problem in transportation networks and introduces a novel
reinforcement learning-based algorithm, namely. Different from almost all canonical sota …

Wireless body area network (WBAN) suffers secure challenges, especially the
eavesdropping attack, due to constraint resources. In this article, deep reinforcement …

This paper investigates the stochastic on-time arrival (SOTA) problem in transportation
networks. We propose a fourth moment approach (FMA), which calculates the tight lower …

Deep Reinforcement Learning (DRL) has proven to be a very strong technique with results
in various applications in recent years. Especially the achievements in the studies in the field …

Storage plays an important part in the field of logistics; however, conventional storage and
operation may result in an overloaded condition due to low management efficiency. To …

Influenced by external factors, the speed of vehicles in the traffic network is changing all the
time, which makes the traditional static shortest route unable to meet the real logistics …

In this study, we propose a general method for tackling the Pickup and Drop-off Problem
(PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our …

In environments with delayed observation, state augmentation by including actions within
the delay window is adopted to retrieve Markovian property to enable reinforcement learning …