A major challenge in the operation of water heating systems lies in the highly stochastic nature of occupant behavior in hot water use, which varies over di,erent buildings and can change over the time. However, the current operational strategies of water heating systems are detached from occupant behavior, and follow a conservative and energy intensive approach to ensure the availability of hot water any time it is demanded. is paper proposes a Reinforcement learning-based control framework which can learn and adapt to the occupant behavior of each speci_c building and make a balance between energy use, occupant comfort and water hygiene. e proposed framework is compared to the conventional approach using the real-world measurements of hot water use behavior in a single family residential building. Although the monitoring campaign has been executed during home lockdown due to COVID-19, when the occupants exhibited a very di,erent schedule and water use related behavior, the proposed framework has learned the occupant behavior over a relatively short period of 8 weeks and provided 24.5% energy use reduction over the conventional approach, while preserving occupant comfort and water hygiene.