The high programmability provided by Software Defined Networking (SDN) paradigm facilitated the integration of Machine Learning (ML) methods to design a new family of network management schema. Among them, we can cite self-driving networks, where ML is used to analyze data and define strategies that are then translated by SDN controllers into network configurations, thus making networks autonomous and capable of auto-scaling decisions based on the network's needs. Despite its attractiveness, the centralized design of the majority of proposed solutions cannot keep up with the increasing size of the network. To this end, this thesis investigates the use of a multi-agent reinforcement learning (MARL) model for auto-scaling decisions in an SDN environment.