Constructing autonomous, personalized, and private working management of smart home products based on deep reinforcement learning

Smart home products have been increasingly popular in markets and design research. Although modern smart home products are interactive and aware, they are not competently autonomous, personalized, and private to satisfy users' experience in convenience and trustworthiness. As a branch of machine learning, deep reinforcement learning is characterized by its learning through interaction with environments without data exchanges and thus has the potential to resolve this problem. This paper proposes a method based on deep reinforcement learning to construct autonomous, personalized, and private working management of smart home products. This method generally splits into users' manual management, training in deep reinforcement learning, and autonomous working management. Besides, this paper illustrates this method with a case study of robot vacuum cleaners. Overall, the contribution of this paper lies in the innovative application of deep reinforcement learning, which dynamically interacts with users' contexts and working conditions, to realize autonomy, personalization, and data privacy of smart home products.