Graph neural networkNeural architecture searchReinforcement learningIn order to improve the accuracy of fault diagnosis, researchers are constantly trying to develop new diagnostic models. However, limited by th
Specifically, researchers represent a given 3D many-body system as a spatial graph. First, they move the centroid of the system into the origin to preserve the translation equivariance. Then, a complete local frame F_ij=(a_ij,b_ij,c_ij) is built...
We propose a deep Q-learning approach [13], in which a Q-function is learned from cell and UE deployment instances and the corresponding reward we get from the network environment. The advantage of the proposed GNN formulation as the neural network for the Q-function is that GNN is scalable...
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systemsieeexplore.ieee.org/abstract/document/9683135 背景 自主按需移动(AMoD)系统是现有交通模式的一种有吸引力的替代方案,目前正受到城市化和日益增长的出行需求的挑战。目前基于学习的AMoD系统控制方法仅限于单个城市的场景,即允许...
suited to model graph-structured in ormation. Recently, Graph Neural Network (GNN) [18] have been proposed to model and operate on graphs with the aim o achieving relational reasoning and combinatorial generaliza- tion. In other words, GNNs acilitate the learning o relations ...
Focusing on the problems, we propose a leader-follower flocking algorithm based on a novel reinforcement learning (RL) model. We construct a homogeneous graph neural network (GNN) based multi-agent deep deterministic policy gradient (herein HGNN-MADDPG) algorithm model for multi-agent flocking ...
Neural Networks via Meta Learning. In International Conference on Learning Representations (ICLR). 综上,我们的框架是本质上不同于现有的工作:不是操纵现有的节点之间的链接,我们的工作加入假节点和操纵假节点的标签和链接生成扰动图。 2.2 Reinforcement Learning in Graph ...
Graph Neural Network to predict the reaction related properties for reinforcement learning - learningmatter-mit/ReactionGraphNeuralNetwork
可以看出整个模型大体分为两个部分,FDNet和GCNN。其中FDNet指的是frame distillation network,帧蒸馏网络,形象的将选取最有用的帧的过程用蒸馏来形容。FDNet得到有用的帧之后就要输入到GCNN,也就是graph convolutional neural network里面进行graph convolution,完成动作识别。
These frameworks incorporate the structure of wireless networks into hierarchical reinforcement learning and spatial graph-based neural network. FIG. 1 illustrates a wireless access network 100 with RL and GNN-based resource management performed by a resource management node (RMN), according to some ...