Ricerc@Sapienza

In our work we investigate different Deep Reinforcement Learning (DRL) approaches to provide a local and proactive network control management to guarantee specific QoS requirements. These requirements are characterized by multiple aspects such as transmission delays, propagation delays, links utilization and failure events. Our idea aims to analyze a specific network scenario, focusing the attention on an aspect listed above, trying to maintain a stable network performance during critical situations. Such situations will be analyzed by a DRL-based framework, in which we will train an agent to react autonomously and proactively to avoid further deterioration in network performance. The agent will be a single node in the network or multi-node in the case of multi-agent, who will have the ability to modify the environment observed through the application of actions (e.g. re-routing operation) that will restore stability to the network, maintaining QoS requirements. Performance evaluations should consider the improvement achieved by the agent (or multi-agent) with respect to the observed critical parameter, and there should be a comparison of this Reinforcement Learning methodology against the heuristic solutions already available. Finally, the differences between single agent and multi-agent will be evaluated, to understand which network and agent location characteristics guarantee improvements in results, maximizing coordinated decision behavior.