Research Article
Research on Modeling of Adaptive Allocation of Labor Resources Based on Deep Reinforcement Learning
@INPROCEEDINGS{10.1007/978-3-031-18123-8_31, author={Bo Sun and Shaoping Zhang}, title={Research on Modeling of Adaptive Allocation of Labor Resources Based on Deep Reinforcement Learning}, proceedings={Multimedia Technology and Enhanced Learning. 4th EAI International Conference, ICMTEL 2022, Virtual Event, April 15-16, 2022, Proceedings}, proceedings_a={ICMTEL}, year={2022}, month={10}, keywords={Deep reinforcement learning Labor resources Self-adaptation Resource allocation}, doi={10.1007/978-3-031-18123-8_31} }- Bo Sun
Shaoping Zhang
Year: 2022
Research on Modeling of Adaptive Allocation of Labor Resources Based on Deep Reinforcement Learning
ICMTEL
Springer
DOI: 10.1007/978-3-031-18123-8_31
Abstract
Based on the problem of the unreasonable allocation of labor resources in my country, a modeling method for adaptive allocation of labor resources based on deep reinforcement learning is proposed, combined with deep reinforcement learning algorithms to calculate the distortion of labor resource allocation in my country's primary, secondary, and tertiary industries degree. Analyzed the changing trend of labor resource allocation in urban and rural areas, and proposed an adaptive allocation plan of labor resources based on my country's industrial development structure in recent years to optimize the allocation structure of labor resources. Finally, it was confirmed by experiments that the adaptive allocation model of labor resources based on deep reinforcement learning It has high practicability and can better integrate the actual situation for effective allocation of labor resources.
