Personalized Health Management Strategies Based on Deep Reinforcement Learning in the Network Environment

Abstract

In order to study the optimal personalized motion push target, the author proposes a personalized health management
strategy based on deep reinforcement learning in the network environment. Firstly, the research problem is defined, and a real-time interactive personalized motion target decision-making model is constructed; Subsequently, in response to the uncertain characteristics of user behavior in the problem, a deep reinforcement learning algorithm was adopted, combined with the departure strategy temporal difference learning method and neural network nonlinear fitting method, in order to learn strategies from user historical data; Finally, the effectiveness of the proposed method was validated using a real dataset from Fitbit. The research results indicate that personalized motion goal push services based on deep reinforcement learning can help users cultivate a healthy lifestyle and improve their personal health management level by analyzing user behavior data in real-time, providing scientific guidance and timely incentives.