The thesis aims to analyze the application of a Deep Reinforcement Learning algorithm to a case study in the field of control. The algorithm chosen is Proximal Policy Optimization, which in recent years has reached the state of the art in various fields of application; the control problem taken into consideration is the powered descent phase of a lander and the subsequent landing in a predetermined area, the Apollo 11 mission was taken as a guideline for some aspects in the creation of the model. The Unity framework was used for the modeling and simulation part, the ML-Agents library for the management of the DRL part.