Traditional routing protocols employ limited information to make routing decisions, leading to slow adaptation to traffic variability and restricted support to applications quality of service requirements. This paper introduces the work developed in the MSc. thesis entitled "Routing based on Reinforcement Learning for Software-Defined Networking", which defines routing approaches based on (deep) reinforcement learning. The results show that our solutions surpass routing algorithms based on Dijkstra as well as they are practical and feasible solutions for routing in Software-Defined Networking.