Intelligent routing decision method based on DDPG reinforcement learning algorithm

Abstract

The invention provides an intelligent routing decision method based on reinforcement learning, in particular to an intelligent routing decision method based on a DDPG reinforcement learning algorithm.The method aims at designing an intelligent routing decision by utilizing reinforcement learning, balancing an equivalent path flow load and improving the processing capacity of a network for burst flow, an experience decision mechanism based on a sampling probability is adopted, the probability that experience with poorer performance is selected is higher, and the training efficiency of an algorithm is improved. In addition, noise is added into neural network parameters, system exploration is facilitated, and algorithm performance is improved. The method comprises the following steps: 1) constructing a network topology structure; 2) numbering equivalent paths in the network topology structure G0; (3) constructing a routing decision model based on a DDPG reinforcement learning algorithm,(4) initializing a flow demand matrix DM and an equivalent path flow proportion matrix PM, and (5) carrying out iterative training on the routing decision model based on reinforcement learning, and the method can be used for scenes 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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