Unmanned aerial vehicle auxiliary resource allocation method based on deep reinforcement learning

Abstract

The invention provides an unmanned aerial vehicle auxiliary resource allocation method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a deep reinforcement learning model, obtaining a neural network, and initializing neural network parameters; s2, obtaining calculation task information generated by the intelligent device and integrating the calculation task information into a system state St; s3, inputting a system state St to train the neural network to obtain a system action At; s4, calculating to obtain the corresponding total overhead Ctotal according to the system action At; s5, training a neural network according to the total overhead Ctotal to obtain a system action for minimizing the total overhead; s6, completing the training ofthe neural network, and carrying out the resource distribution according to the obtained system action enabling the total overhead to be minimized. The invention provides an unmanned aerial vehicle auxiliary resource allocation method based on deep reinforcement learning, and solves the problems of relatively high calculation task time delay and energy consumption of existing industrial Internetof Things intelligent equipment.

Description

technical field [0001] The present invention relates to the technical field of resource allocation optimization, and more specifically, to a UAV-assisted resource allocation method based on deep reinforcement learning. Background technique [0002] Industry is an important field of IoT applications. Various smart devices with environmental awareness, computing models based on ubiquitous technology, and mobile communications are continuously integrated into all aspects of industrial production, which can greatly improve manufacturing efficiency, improve product quality, and reduce Product cost and resource consumption accelerate the transformation of traditional industries to intelligence. [0003] Wireless smart device networks have been widely used in many fields such as field or industry. In these scenarios, smart devices are often limited in terms of battery power due to their small form factor and strict production cost constraints. Relying on traditional energy supply,...

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Problems solved by technology

[0006] The present invention provides a UAV auxiliary resource allocation method based on deep reinforcement learning in order to overcome th

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