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...
Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use caseRecent advances in Deep Reinforcement Learning (DRL) have shown a significant\nimprovement in decision-making problems. The networking community has started\nto investigate how DRL can provide a new breed ...
系统标签: graph neural networks drl learning deep DeepReinforcementLearningmeetsGraphNeuralNetworks:AnopticalnetworkroutingusecasePaulAlmasanBarcelonaNeuralNetworkingCenterUniversitatPolitècnicadeCatalunyaJoséSuárez-VarelaBarcelonaNeuralNetworkingCenterUniversitatPolitècnicadeCatalunyaArnauBadia-SamperaBarcelonaNeuralNetworking...
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 ...
graph sizes and can capture local network features with variable numbers of cells and UEs. To make the best selection for UE connectivity, we need to learn the right Q-function. As the Q-function is captured through the GNN, this translates to learning the parameters of the GNN which we ...
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 ...
Deep reinforcement learning(DRL) methods have not been applied to this research area. This paper presents an innovative method using DRL with graph neural network (GNN) to solve FJSPLS with equal and consistent sub-lots. First, to facilitate integrated decision-making for the sub-problems, two ...
1) Q-table (具体可参考:Reinforcement Learning Stock Trader) 个人认为这不是一个很好的办法,因为Q-table里面的state是有限的,而我们定义的state里面的数据往往都是连续的,很难在有限个state里面去很好的表达。 2) Deep Q Network(参考:Reinforcement Learning for Stock Prediction) 在1的基础上,将Q-table的...
financial signal representation and trading[J]. IEEE transactions on neural networks and learning ...
information-centric networking; software-defined networking; graph neural network; deep reinforcement learning; intelligent caching1. Introduction Information-centric networking (ICN) has received significant interest and attention in recent years as a promising paradigm for network communication. ICN introduces...