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Network slicing is a critical technique for 5G communications that coversradio access network (RAN), edge, transport and core slicing.The evolvingnetwork architecture requires the orchestration of multiple network resourcessuch as radio and cache resources. In recent years, machine learning (ML)techniques have been widely applied for network management. However, mostexisting works do not take advantage of the knowledge transfer capability inML. In this paper, we propose a deep transfer reinforcement learning (DTRL)scheme for joint radio and cache resource allocation to serve 5G RAN slicing.We first define a hierarchical architecture for joint resource allocation. Thenwe propose two DTRL algorithms: Q-value-based deep transfer reinforcementlearning (QDTRL) and action selection-based deep transfer reinforcementlearning (ADTRL). In the proposed schemes, learner agents utilize expertagents' knowledge to improve their performance on current tasks. The proposedalgorithms are compared with both the model-free exploration bonus deepQ-learning (EB-DQN) and the model-based priority proportional fairness andtime-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposedDTRL-based method presents 21.4% lower delay for Ultra Reliable Low LatencyCommunications (URLLC) slice and 22.4% higher throughput for enhanced MobileBroad Band (eMBB) slice, while achieving significantly faster convergence thanEB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput areobserved with respect to PPF-TTL.

Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing