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A Packet Routing Algorithm Based on Multi-Agent Deep Reinforcement Learning

A multi-agent, reinforcement learning technology, applied in the direction of data exchange network, digital transmission system, biological neural network model, etc., to achieve the effect of short data packet transmission delay and excellent performance

Active Publication Date: 2021-08-17
FUDAN UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, literature [12] considers a centralized data flow routing strategy, and requires global topology information and traffic demand matrix

Method used

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  • A Packet Routing Algorithm Based on Multi-Agent Deep Reinforcement Learning
  • A Packet Routing Algorithm Based on Multi-Agent Deep Reinforcement Learning
  • A Packet Routing Algorithm Based on Multi-Agent Deep Reinforcement Learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0051] Set the parameters of the example

[0052] Simulation environment: Python;

[0053] Network topology: such as figure 2 shown;

[0054] Data packet transmission interval: 0.3 ~ 1.0ms;

[0055] Data packet distribution ratio: 10% to 90%;

[0056] Experience return visit pool size: 100;

[0057] Learning rate: 0.001.

[0058] A packet routing algorithm based on multi-agent deep reinforcement learning, the specific steps are:

[0059] Step 1: Initialize the experience playback pool of each router, and initialize each neural network randomly.

[0060] Step 2: Router n observes local information d p and E n , collect shared information C n . Synthesize current state s t :{d p ,E n ,C n} and the hidden state h t , select action a according to the ∈-greedy strategy t .

[0061] Step 3: Router n transmits data packet p to corresponding adjacent node v t , while receiving the reward r t . The current state and the hidden state are respectively transformed int...

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Abstract

The invention belongs to the technical field of distributed routing, and specifically relates to a data packet routing algorithm based on multi-agent deep reinforcement learning. In order to alleviate the congestion in the computer network, the present invention uses multi-agent deep reinforcement learning technology to design an end-to-end adaptive routing algorithm, and each router completes data packet scheduling according to local information, reducing the transmission delay of data packets . The invention first constructs a mathematical model of distributed routing, defines the specific meaning of each element in reinforcement learning, then trains a neural network, and finally performs algorithm performance testing in a simulation environment. The simulation experiment results show that the introduction of the deep neural network can mine the characteristic information in the input network state and realize the trade-off between the smooth path and the shortest path. Compared with other commonly used routing algorithms, the present invention achieves a shorter data packet transmission time delay.

Description

technical field [0001] The invention belongs to the technical field of distributed routing, and in particular relates to a data packet routing algorithm based on multi-agent deep reinforcement learning. Background technique [0002] Packet routing is a very challenging problem in distributed computer networks, especially in wireless networks of service providers that lack centralized control. In order to minimize the transmission delay, each router needs to determine the next hop node to transmit its data packet. The first and foremost feature of packet routing is a fine-grained packet forwarding policy. Network traffic information cannot be shared between adjacent nodes. Existing routing protocols use flooding strategies to maintain a globally consistent routing table (such as DSDV algorithm [1]), or build on-demand traffic-level routing tables (such as AODV algorithm [2]). Packet routing needs to meet the dynamically changing traffic in current communication networks. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/721G06N3/04
CPCH04L45/14H04L45/12G06N3/045
Inventor 徐跃东游新宇李宣洁
Owner FUDAN UNIV
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