… ranking and embedding strategies. Some reinforcement learning aided embedding algorithms
… -making strategies, while the node embedding of the same request is discretized and its …

… embedding solutions in an acceptable running time. In this paper, we combine deep
reinforcement learning … algorithm for automatic virtual network embedding. In addition, a parallel …

… Reinforcement Learning based prediction model is designed for the efficient Multi-stage Virtual
Network Embedding … In this section, the MUVINE scheme is described, which is designed …

… handle the dynamic SFC embedding. In this paper, with recent advances in deep reinforcement
learning (DRL) [13], we present a DRL-based SFC embedding scheme in NFV-enabled …

… In this section, we evaluate our embedding scheme on several benchmark problems in the
Dec-POMDP literature [31]: Grid3x3corners [32], Dectiger [33], and Boxpushing [34]. We first …

… to deep reinforcement learning (DRL) [7], we present a DRL-based SFC embedding scheme
in … DRL is able to input high-dimensional data and output an optimal policy for the learning …

… 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 optimal …

… In this work, we propose a Reinforcement-Learning-based algorithm able to process data
that the customer and ISPs cipher under the Shamir Secret Sharing (SSS) scheme. This …

… , we adopt the Deep Reinforcement Learning (DRL) to optimize the SFC embedding. Fig. 1
… DRL-based schemes to train the network model. The DRL agent action including the VNF …

… methods, whose static embedding strategies cannot be … In this article, we propose a deep
reinforcement learning (DRL) … embedding scheme relying on reinforcement learning,” IEEE …