The scope of this Master Thesis is to present a new application of an algorithm based on Reinforcement Learning (RL) for the control of HVAC systems to reduce the energy consumption. The aim of the analysis is to assess the adaptability of the RL algorithm to different operating conditions and to compare its performance against state of the art HVAC control strategies. The RL algorithm is allowed to choose the air temperature set-point of HVAC system within a band using as input, signals from sensors that are usually part of HVAC equipment and a weather forecast. Since RL algorithm learns online by interacting with an environment, the possibility offered by simulation tools is very attracting to study and develop such algorithms in the field of building energy management systems.