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

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 …

In wireless networks, context awareness and intelligence are capabilities that enable each
host to observe, learn, and respond to its complex and dynamic operating environment in an …

OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes a large number
of well-known problems that expose a common interface allowing to directly compare the …

The dynamicity of distributed wireless networks caused by node mobility, dynamic network
topology, and others has been a major challenge to routing in such networks. In the …

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

This paper proposes a multi-agent reinforcement learning based medium access framework
for wireless networks. The access problem is formulated as a Markov Decision Process …

Recently, we have seen a boom of attempts to improve the operation of networking protocols
using machine learning techniques. The proposed reinforcement learning (RL) based …

Designers face many system optimization problems when building distributed systems.
Traditionally, designers have relied on optimization techniques that require either prior …