… introduce a reinforcement learning method to virtual network … a policy network based on
reinforcement learning to make … policy network with the historical data based on virtual network …

… to better virtual network embedding decisions. Specifically, we formulate adaptive online
VNE as a deep reinforcement learning (DRL) [10] problem, and design an algorithm to learn the …

… deep reinforcement learning with a novel neural network structure based on graph
convolutional networks, and propose a new and efficient algorithm for automatic virtual network …

… In this work, we solve the VNF-FGE problem by means of a Reinforcement Learning approach
to model a placement policy in a Network Function Virtualization infrastructure. For that …

… Then, we propose a solution based on deep reinforcement learning (DRL). More precisely,
we present a novel RL approach, called parameterized action twin (PAT) deterministic policy …

… multilayer VNE algorithm in SDN based on reinforcement learning. The main contributions of
this … physical network and upper VNRs to under VNRs. 2) We build a reinforcement learning …

… -core networks. Specifically, our framework is based on a deep reinforcement learning (DRL) …
, ie it is capable of learning which slice to admit based on the availability of the physical …

… of virtual network request (VNR) … reinforcement learning (RL) and graph neural network
(GNN) theory to the algorithm, especially the combination of graph convolutional neural network (…

… on reinforcement learning (RL). In the training phase, we use a policy network as a learning
… The learning agent is trained in this environment to get the mapping probability of each …

… reinforcement learning for the selection of alternate resources to satisfy the QoS requirement.
The reinforcement learning [7… Therefore, we propose to utilize the reinforcement learning to …