… opportunity for the management of network slicing resources. Leveraging the knowledge …
a novel Machine Learning-based scheme for dynamic resource scheduling for networks 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 …

… work, deep reinforcement learning and relational graph convolutional neural networks in order
… Simulation results show the effectiveness of our approach. Starting with an initial solution …

… principle of network slicing, state the challenges faced by the RAN slicing, and then discuss
the importance of applying deep reinforcement learning (DRL) in the context of RAN slicing. …

… NR C-V2X sidelink communication based on network slicing and NOMA. To do so, we apply
deep reinforcement learning (DRL) [24]. In general, deep learning (DL) has had significant …

… to apply DRL in network slicing from a general perspective. … of policy training still lacks the
necessary learning speed. For example, our pre-training for the priority-based network slicing …

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

… network into multiple slices supporting independent services. In beyond 5G (B5G) systems,
the main goal of network slicing … a deep reinforcement learning (DRL)-based network slicing …

… reinforcement learning based approach for network slicing. … reinforcement learning
algorithms for network slicing under … to apply constrained RL for network slicing. Specifically, to …

… We present a fast-learning DRL model that can dynamically optimize the network slice
configuration in Wi-Fi networks. Our network slices require each a different network configuration. …