Research on Intelligent Selection Mode of Edge Server Based on Artificial Intelligence Deep Reinforcement Learning Algorithm

Based on the artificial intelligence deep reinforcement learning algorithm, this paper proposes an intelligent selection mode with high fairness, expansibility and intelligence. On the basis of the artificial intelligence deep reinforcement learning algorithm, innovative mechanisms such as action inhibition, quadruple Q-learning (QQL) and normalized Q-value are introduced. With the research results of this paper, the IoT (Internet of Thing) terminal can more intelligently select its access or handover edge server under the principle of meeting the service delay requirements and fairness. This scheme reduces service delay, improves service response efficiency, and has good value significance for improving service security and operation management level.