… resources into multiple virtual networks. 2) We discuss the network slice optimization
problem. A DDPG-based slice optimization algorithm is designed for the sake of searching the …

… Abstract—Deep reinforcement learning (DRL) has been increasingly employed to handle …
management in network slicing. The deployment of DRL policies in real networks, however, is …

… transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation
to serve 5G RAN slicing. … part of network slicing, radio access network (RAN) slicing is more …

… are considered part of the endto-end network slicing, each with a slightly different
optimization goal. In this paper, we mainly focus on the RAN part of network slicing. RAN slicing …

… ,17] and genetic algorithms [18], this paper surveys network slicing resource management
approaches based on reinforcement learning (RL) and DRL techniques. RL and DRL will play …

… However, TL-assisted MADRL in inter-cell network slicing scenarios is still an … in network
slicing with distributed MADRL by extending our previous work [1]. Our objective is to optimize …

… Deep RL and the 5G network slicing research, by presenting a … 5G network slicing and
virtualization principles are then discussed. Thirdly, we review challenges in 5G network slicing …

… , each with a partially different optimization goal [10]. The main focus of this paper is on the
… of network slicing, illustrated in Fig. 1. RAN slicing involves two primary RRM functions: slice …

… dynamic control based on the network conditions and service requirements such as network
slicing [3]. In NGNs, the optimization domains and network requirements are expected to …

… (QoS) due to fluctuations in the network demand. To address this issue, we … for network
slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning …