Deep Reinforcement Learning-Based Access Control for Buffer-Aided Relaying Systems With Energy Harvesting

This paper considered a buffer-aided relaying system with multiple source-destination pairs and a relay node (RN) with energy harvesting (EH) capability. The RN harvests energy from the ambient environment and uses the harvested energy to forward the sources' information packet to the corresponding destinations. It is assumed that information on the EH and channel gain processes is unavailable. Thus, the model free deep reinforcement learning (DRL) method, specifically the deep Q-learning, is applied to learn an optimal link selection policy directly from historical experience to maximize the system utility. In addition, by taking advantage of the structural features of the considered system, a pretraining scheme is proposed to accelerate the training convergence of the deep Q-network. Experiment results show that the proposed pretraining method can significantly reduce the training time required. Moreover, the performance of the transmission policy obtained by using deep Q-learning is compared with that of several conventional transmission schemes. It is shown that the transmission policy obtained by using our proposed model can achieve better performance.