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Abstract:
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In the context of modern high-speed communication networks, decision reactivity is
often complicated by the notion of guaranteed Quality of Service (QoS), which can either be
related to time, packet loss or bandwidth requirements: constraints related to various types of QoS
make some algorithms not acceptable. Due to emerging real-time and multimedia applications,
efficient routing of information packets in dynamically changing communication network requires
that as the load levels, traffic patterns and topology of the network change, the decision policy also
adapts. We focused in this paper on QoS based mechanisms by developing a neuro-dynamic
programming to construct dynamic state-dependent policies. In this paper, we present an accurate
description of the current state- of-the-art and give an overview of our work in the use of
reinforcement learning concepts focused on communications networks. We focus our attention by
developing a system based on this paradigm and study the use of reinforcement learning
approaches in three different communication networking domains: wired networks, mobile ad hoc
networks, and packet router’s scheduling networks |