Mobile edge computing offload method based on multi-agent reinforcement learning

Abstract

The invention discloses a mobile edge computing offloading method based on multi-agent reinforcement learning, which belongs to the field of edge computing and wireless networks, and provides an intelligent task offloading method for complex scenarios of "multi-user-multi-edge nodes". This method adopts a multi-agent reinforcement learning algorithm. Each user device establishes an Actor and Critic deep learning network locally, and performs action selection and action scoring according to the state and actions of itself and other devices, and comprehensively considers spectrum resources, computing resources, and energy resources. , formulate offloading and resource allocation strategies with the goal of optimizing task latency. This method does not depend on the specific model of the network, and each device can autonomously and intelligently formulate the optimal strategy through the learning process of "exploration-feedback", and can adapt to the dynamic changes of the network environment.

Description

technical field [0001] The invention belongs to the field of edge computing and wireless network, and relates to a computing unloading method based on multi-agent deep reinforcement learning, in particular to computing task unloading strategies and multi-dimensional resource joint allocation problems. Background technique [0002] With the continuous development of mobile Internet technology, computing-intensive emerging applications such as virtual reality, online games, face recognition, and image processing are rapidly emerging. However, the popularity of these computing-intensive applications is limited due to the limited computing power of terminal devices. To solve this problem, cloud computing emerged as the times require, which uploads complex computing tasks on the terminal device side to cloud servers with more powerful computing capabilities for execution, so as to relieve the dependence of these emerging applications on device computing capabilities. However, tr...

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Problems solved by technology

However, this kind of decision-making optimization problem is usually NP-hard, especially when the network scale is large, even through the heuristic solution algorithm, it still takes a long calculation time to obtain the optimal strategy

In addition, the state of the network is usually changing dynamically, which requires the central node to continuously solve complex optimization problems, and it is difficult to adaptively track the dynamic environment of the network

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