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

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...

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

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