This thesis investigates the application of Reinforcement Learning techniques to Building Energy Management Systems (BEMS), with a focus on the sensitivity of learning performance to training strategies and data diversity. After reviewing the current state of the art, the study analyses how different training configurations affect the agent’s ability to learn and generalise control policies. Two training approaches are compared. In the first, the environment resets at the end of each episode, with the simulation restarting from the same initial date and weather conditions. In the second approach, the reset is ignored, enabling the agent to experience temporal continuity and different outdoor and operating conditions across episodes.