Optimizing Employee Skill Development and Competency Mapping with Deep Reinforcement Learning and Knowledge Graphs in Human Resource Management

Somantri

Volume: 11 Issue: 01 2025

Abstract:

Employee competency mapping and skill development are imperative in contemporary organizations to enhance productivity, career growth, and employee retention. The current paper introduces a high-end framework that leverages Deep Reinforcement Learning (DRL) and Knowledge Graphs to enhance employee competency mapping and skill development. The proposed framework makes use of the IBM HR Analytics Employee Attrition & Performance dataset containing key data points like performance rating, training hours, attrition status, and demographic details. DRL model dynamically suggests tailor-made career growth and development plans to employees based on employee performance and individual skill set, maximizing the growth of individual employees. Knowledge Graph module integrates intra association among diverse skills, job roles, and worker performance so that a macro-level picture of organizational capacity is achieved. With convergence of these technologies, the system increases the possibility of forecasting careers of employees, determining training requirements, and boosting retention levels. The method delivers a more efficient, data-driven, and scalable human resource management solution for firms to maximize employee development alignment with business goals. The suggested approach is a new entrant to the optimization of HR practice and can be used across various industries in order to facilitate employee development and organizational success.

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