An Intelligent Routing Decision-Making Method Based on DDPG Reinforcement Learning Algorithm

Abstract

The present invention proposes an intelligent routing decision-making method based on reinforcement learning, and specifically relates to an intelligent routing decision-making method based on DDPG reinforcement learning algorithm, which aims to design intelligent routing decisions by using reinforcement learning to balance the equivalent path traffic load, To improve the ability of the network to handle burst traffic, the present invention adopts an experience decision-making mechanism based on sampling probability, and the experience with poorer performance has a greater probability of being selected, thereby improving the training efficiency of the algorithm. In addition, adding noise to the neural network parameters is beneficial to the exploration of the system and improves the performance of the algorithm. The implementation steps are: 1) constructing the network topology; 2) constructing the network topology G 0 3) Construct a routing decision model based on the DDPG reinforcement learning algorithm; 4) Initialize the traffic demand matrix DM and the equivalent path traffic ratio matrix PM; 5) Iterate the routing decision model based on reinforcement learning train. The present invention can be used in scenarios such as a data center network.

Description

technical field [0001] The invention belongs to the technical field of computer networks, and relates to an intelligent routing decision-making method based on reinforcement learning, in particular to an intelligent routing decision-making method based on a DDPG reinforcement learning algorithm, which can be used in fields such as data center networks. Background technique [0002] For the network, the routing decision is the process of determining the specific forwarding path of the data packet in the network. It specifies how the data flow reaches another node from a specified node in the network. The routing decision determines the load balance of the equivalent path in the network. , which is mainly related to the difference in bandwidth utilization of each equivalent path and whether deadlock occurs. The smaller the difference, the worse the load balance of the equivalent path, and the occurrence of deadlock will also lead to the deterioration of the load balance of the ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose an intelligent routing decision-making method based on a reinforcement learning algorithm, which is used to solve the technical problem of poor load balance of equivalent paths existing in the prior art

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