Resource allocation method for industrial wireless network based on multi-agent deep reinforcement learning

Abstract

The present invention relates to industrial wireless network technology, specifically, an industrial wireless network resource allocation method based on multi-agent deep reinforcement learning, including the following steps: establishing an industrial wireless network with terminal-edge coordination; establishing industrial wireless network terminal-edge resources Allocation optimization problem; establish Markov decision model; use multi-agent deep reinforcement learning method to construct resource allocation neural network model; offline training neural network model until the reward converges to a stable value; based on the offline training results, industrial wireless network online Perform resource allocation, process industrial tasks. The present invention can implement end-edge collaborative resource allocation for industrial wireless networks in real time and with high energy efficiency, and minimize system overhead under the constraints of limited energy and computing resources.

Description

technical field [0001] The invention relates to resource allocation under the constraints of limited energy and computing resources, belongs to the technical field of industrial wireless networks, and specifically relates to an industrial wireless network resource allocation method based on multi-agent deep reinforcement learning. Background technique [0002] With the development of Industry 4.0, a large number of distributed industrial terminals are interconnected through industrial wireless networks, resulting in massive calculation-intensive and delay-sensitive industrial tasks. However, the local energy and computing resources of industrial terminals are limited, and it is difficult to meet the quality of service requirements of industrial tasks. [0003] The edge computing server deployed on the edge of the network can provide nearby computing resource support for industrial terminals, but the large-scale concurrent offloading of industrial terminals will cause full lo...

AI Technical Summary

Problems solved by technology

However, industrial terminals are mobile, and the amount of energy and computing resources is time-varying. It is difficult for a single agent to track the dynamic changes of system information. At the same time, collecting global system information by a single agent will increase time delay and energy consumption.

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