The CLSDRL model consists of deep deterministic policy gradients (DDPG) deep couple with convolution neural network (CNN) and long-short memory neural network (LSTM). After extracting the spatial and temporal characteristics of network traffic with CNN and LSTM, routing decisions are made with DDPG algorithm.
In this paper, a routing optimization method based on deep reinforcement learning by feature extraction is proposed for the spatial and temporal characteristics ...
This paper describes comparisons of traffic safety during the morning and afternoon peak hours in extended stretches of eight High Occupancy Vehicle (HOV) ...
Traffic Optimization and Optimal Routing in 5G SDN Networks Using Deep Learning ... CLSDRL:A routing optimization method for traffic feature extraction.
CLSDRL: A routing optimization method for traffic feature extraction. NaNA ... Web Service Selection Algorithm Based on Particle Swarm Optimization. DASC 2009: ...
Oct 24, 2022 · This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address ...
Missing: CLSDRL: | Show results with:CLSDRL:
CLSDRL:A Routing Optimization Method for Traffic Feature Extraction. 260. Hong Xia (Xi'an University of Posts and Telecommunications, China;. Shaanxi Key ...
CLSDRL: A routing optimization method for traffic feature extraction. 260-264. view. electronic edition via DOI · unpaywalled version · references & citations.
CLSDRL:A Routing Optimization. Method for Traffic Feature Extraction. Short. 9. 16:30-16:45. Yusuke Noda and Bishnu P. Gautam. A Proposal of Large Scale Network.
Nov 30, 2020 · Here we introduce a method to extract network topologies from dynamical equations related to routing optimization under various parameters' settings.
Missing: CLSDRL: traffic feature