Method for controlling routing actions based on multi-agent reinforcement learning routing strategy

A reinforcement learning and multi-agent technology, applied in the information field, can solve the problem of reducing the average delivery time of data packets, and achieve the effect of reducing the average delivery time

Active Publication Date: 2020-07-14
SHENZHEN RES INST OF BIG DATA +1
View PDF8 Cites 20 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These simulated network models ignore many important network characteristics, such as dynamically changing network loads and mobile users, so the routing choices made under these models often cannot minimize the average delivery time of data packets

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for controlling routing actions based on multi-agent reinforcement learning routing strategy
  • Method for controlling routing actions based on multi-agent reinforcement learning routing strategy
  • Method for controlling routing actions based on multi-agent reinforcement learning routing strategy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0047] In the present disclosure, it should be understood that terms such as "comprising" or "having" are intended to indicate the presence of features, numbers, steps, acts, components, parts or combinations thereof disclosed in the specification, and are not intended to exclude one or a plurality of other features, numbers, steps, acts, parts, parts or combinations thereof exist or are added.

[0048] In addition, it should be noted that, in the case of no conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and embodiments.

[0049] figure 1 A flowcha...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to the technical field of information, and discloses a method for controlling routing actions based on a multi-agent reinforcement learning routing strategy. The method comprisesthe following steps: training a reinforcement learning model, updating decision values of routing nodes by the reinforcement learning model by using a Q learning algorithm, and updating strategy parameters by using a strategy gradient algorithm in combination with the updated decision values; determining strategy parameters by using the reinforcement learning model according to the target node towhich the routing nodes forward the data packets and the network load in the communication network where the routing nodes is located; and determining an outgoing link of the routing nodes accordingto the strategy parameter. According to the method, routing strategies can be adjusted in time for dynamically changing network connection modes, network loads and routing nodes; the appropriate shortest path is selected according to the target node of the data packet, and finally the average delivery time of the data packet is greatly shortened.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a method for controlling routing actions based on multi-agent reinforcement learning routing strategies. Background technique [0002] Packet routing in communication networks is an important application problem in sequential decision making. A communication network consists of a set of nodes and the links connecting these nodes. Data center networks and the Internet can be seen as real-world examples of communication networks. In a communication network, information is passed between nodes in the form of data packets. Routing is the decision-making process that guides how data packets pass through a series of intermediate nodes from the initial node to the target node. Usually, there are multiple paths for a data packet to choose from in the communication network, and the choice of the path usually determines the average delivery time of the data packet. [0003] At pres...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/721H04L12/725H04L12/751H04L45/02
CPCH04L45/08H04L45/02H04L45/3065H04L45/38
Inventor 陈怿曾思亮许行飞
Owner SHENZHEN RES INST OF BIG DATA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap