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Information interaction method for improving multi-agent reinforcement learning edge calculation effect

An edge computing and reinforcement learning technology, applied in computing, program control design, multi-program device, etc., can solve problems such as low performance, reduce energy consumption, reduce completion delay, and improve user experience

Active Publication Date: 2021-11-12
TIANJIN UNIV
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Problems solved by technology

[0005] Aiming at the low performance of MADRL in resource management and computing offloading in the prior art, the present invention proposes an information interaction method for improving the edge computing effect of multi-agent reinforcement learning

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  • Information interaction method for improving multi-agent reinforcement learning edge calculation effect
  • Information interaction method for improving multi-agent reinforcement learning edge calculation effect
  • Information interaction method for improving multi-agent reinforcement learning edge calculation effect

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Embodiment Construction

[0070] Next, the technical solutions in the embodiments of the present invention will be described in connection with the drawings of the embodiments of the present invention, and it is understood that the described embodiments are merely the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained in the art without paying creative labor, all of the present invention.

[0071] In order to deal with the problem of insufficient processing capacity of terminal equipment, limited resources, the industry introduces computing uninstall concepts in the moving edge calculation (MEC). Edge Calculation Unloading, Calculating the unloading is a key technique in MEC, mainly including two parts of uninstall decision making and resource allocation, which reasonably arranges the user terminal to uninstall the computing task to the MEC server, and allocate the resources for task calculation. Reduce the del...

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Abstract

The invention discloses an information interaction method for improving a multi-agent reinforcement learning edge calculation effect. The information interaction method comprises the following steps of constructing an edge calculation communication model based on a partially observable Markov decision process, establishing a shared memory space for executing memory reading, memory filling and memory writing operations on each edge node, setting a target optimization function according to a user cost minimization target and an edge node utility maximization target, setting a time slot length, a time frame length, an initialization time slot and a time frame, obtaining a resource allocation strategy of the edge node, and executing a memory filling operation, enabling the user to execute memory reading and memory writing operations, obtaining a calculation task, a calculation task data volume and calculation capability of each user at the same time, and obtaining a task unloading strategy of the calculation user, optimizing the target optimization function by using a participant-criminator model, and dividing and processing the calculation task. The decision effectiveness of the edge node and the user can be maximized.

Description

Technical field [0001] The present invention belongs to the field of edge calculation, and in particular to an information interaction method for improving the calculation of multi-intelligent body strength learning edge computing effects. Background technique [0002] With the continuous advancement of science and technology and industrial production capacity, the calculation and communication capacity of mobile devices continues to improve, but all kinds of new mobile applications have put forward higher business needs for mobile devices. In a cluster of a multilateral edge, the user can choose to calculate or uninstall the task locally or unload to the edge device. To respond to innovative applications and user experience growing demand, the uninstallation will calculate the intensive task from the user to the edge. The calculation capability of the edge device is generally stronger than the user, so the user may obtain some time delay and power consumption by uninstalling the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50
CPCG06F9/5072Y02D30/70
Inventor 刘志成李沅泽赵云凤宋金铎王晓飞仇超
Owner TIANJIN UNIV
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