Recent reinforcement learning approaches for continuous control in wireless mobile
networks have shown impressive results. But due to the lack of open and compatible …

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …

Abstract We present the Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet
Gym) an open-source architecture designed for accelerating research and development of …

Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …

Rapid adoption of mobile devices, coupled with the increase in prominence of mobile
applications and services, resulted in unprecedented infrastructure requirements for mobile …

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …

Mobile networks are composed of many base stations and for each of them many
parameters must be optimized to provide good services. Automatically and dynamically …

There is a phenomenal burst of research activities in machine learning and wireless
systems. Machine learning evolved from a collection of powerful techniques in AI areas and …

There is a growing interest in applying deep reinforcement learning (DRL) methods to
optimizing the operation of wireless networks. In this paper, we compare three state of the art …

This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …