Traditional routing protocols employ limited information to make routing decisions, which
can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality …

Traditional routing protocols employ limited information to make routing decisions, which
leads to slow adaptation to traffic variability and restricted support to the quality of service …

With an exponential increase in network traffic demands requiring quality of services, the
need for routing optimization has become more prominent. Recently, the advent of software …

In conventional networks, there was a tight bond between the control plane and the data
plane. The introduction of Software-Defined Networking (SDN) separated these planes, and …

This paper proposes DROM, a deep reinforcement learning mechanism for Software-
Defined Networks (SDN) to achieve a universal and customizable routing optimization …

Routing optimization has been researched in network design for a long time, and plenty of
optimization schemes have been proposed from both academia and industry. However …

In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes
routing. Our agent adapts automatically to current traffic conditions and proposes tailored …

In recent years, with the rapid development of current Internet and mobile communication
technologies, the infrastructure, devices and resources in networking systems are becoming …

Communication networks are difficult to model and predict because they have become very
sophisticated and dynamic. We develop a reinforcement learning routing algorithm …

Traditional routing schemes usually use fixed models for routing policies and thus are not
good at handling complicated and dynamic traffic, leading to performance degradation (eg …