… embedding decisions. To solve this problem, we introduce a reinforcement learning method
to virtual network embedding. … policy network based on reinforcement learning to make node …

… However, there is no intelligent algorithm to solve the security problem of … algorithm based
on reinforcement learning (RL). In the training phase, we use a policy network as a learning …

… most existing algorithms fail to provide automatic embedding … In this paper, we combine
deep reinforcement learning with a … efficient algorithm for automatic virtual network embedding. …

… In this paper, we explore the potential of reinforcement learning techniques for the placement
of VNF-FGs. However, it turns out that even the most well-known learning technique is …

… algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory
to the algorithm, … neural network (GCNN) and RL algorithm. Based on a self-defined fitness …

… In this work, we presented the design and evaluation of an adaptive online virtual network
embedding algorithm based on deep reinforcement learning. A key feature of our design is …

… -layer VNE algorithm combining with reinforcement learning to solve the embedding of multi-…
2) We build a reinforcement learning model named MLRLModel with the goal of increasing …

… • To learn our policy and value networks, we introduce an actor-critic reinforcement
learning algorithm driven by visual-semantic embedding. Our experiments suggest that the …

… learning aided embedding algorithm are used for benchmarking our prpposed CDRL algorithm…
Hence, we introduce the reinforcement learning algorithm [30] to solve this problem. As is …

… on the process of embedding infeasible VNRs (ie, VNRs that cannot be embedded completely).
… virtual network embedding algorithm based on hierarchical reinforcement learning, VNE-…