Deep reinforcement learning (DRL) is a powerful approach that combines reinforcement
learning (RL) and deep learning to address complex decision-making problems in high …

We propose a method for learning multi-agent policies to compete against multiple
opponents. The method consists of recurrent neural network-based actor-critic networks and …

An increasing number of emerging applications, eg, Internet of Things (IoT), vehicular
communications, augmented reality, and the growing complexity due to the interoperability …

The research about unmanned aerial vehicle (UAV) swarm has developed rapidly in recent
years, especially the UAV swarm with sensors which is becoming common means of …

With the development of Machine-to-Machine (M2M) technology, a variety of embedded and
mobile devices is integrated to interact via the platform of the Internet of Things, especially in …

The essence of network security is the asymmetric online confrontation with the partial
observable cyber threats, which requires the defense ability against unexpected security …

In order to improve the communication rate of the cyber–physical fusion system, and at the
same time guarantee the quality of system demand of each user, a CPS (Cyber–Physical …

Spanking information retrieval in large-scale Web and network has attracted increasing
interest in the research community, many typical approaches have been recalled such as …

An increasing number of emerging applications, eg, internet of things, vehicular
communications, augmented reality, and the growing complexity due to the interoperability …

Unmanned aerial vehicles (UAVs) path planning has received significant attention from both
military and academia domains, current researches are more focus on how to implement …