Reinforcement Learning for Autonomous Traffic Flow Capacity Management

Abstract

As the dynamicity of the traffic increases, the need for self-network operation becomes more evident. One of the solutions that might bring cost savings to network operators, is that of the dynamic capacity management of large packet flows, especially in the context of packet over optical networks. Machine Learning, and particularly Reinforcement Learning (RL), seem to be an enabler for autonomicity, as a result of its inherent capacity to learn from experience. In this tutorial, we introduce RL and review its application for autonomous capacity management of traffic flows.