This thesis aims to develop an autonomous navigation system for indoor scenarios based on Deep Reinforcement Learning (DRL) technique. Autonomous navigation is a hot challenging task in the research area of robotics and control systems, which has been tackled with numerous contributions and different approaches. Among them, learning methods have been investigated in recent years due to the successful spreading of Artificial Intelligence and Machine Learning techniques. In particular, in reinforcement learning an agent learns by experience, i.e. through the interaction with the environment where it is placed, avoiding the need of a huge dataset for the training process. Service robotics is the main focus of the research at PIC4SeR (PoliTo Interdepartmental Centre for Service Robotics), where the idea of this thesis project is born as part of a broader project.