  Smart homes require every device inside them to be connected with each other
at all times, which leads to a lot of power wastage on a daily basis. As the
devices inside a smart home increase, it becomes difficult for the user to
control or operate every individual device optimally. Therefore, users
generally rely on power management systems for such optimization but often are
not satisfied with the results. In this paper, we present a novel
multi-objective reinforcement learning framework with two-fold objectives of
minimizing power consumption and maximizing user satisfaction. The framework
explores the trade-off between the two objectives and converges to a better
power management policy when both objectives are considered while finding an
optimal policy. We experiment on real-world smart home data, and show that the
multi-objective approaches: i) establish trade-off between the two objectives,
ii) achieve better combined user satisfaction and power consumption than
single-objective approaches. We also show that the devices that are used
regularly and have several fluctuations in device modes at regular intervals
should be targeted for optimization, and the experiments on data from other
smart homes fetch similar results, hence ensuring transfer-ability of the
proposed framework.
