Adaptive talent journey: Optimization of talents’ growth path within a company via Deep Q-Learning

In enterprise context, companies constantly aim to optimize their human resources and acquire new ones. Employees, also called , are required to achieve new skills for the company to stay competitive in the business. The talents’ ability to productively improve is a crucial factor for the success of a company.We propose , a novel method for optimizing the growth path of talents within a company. The ultimate goal of Adaptive Talent Journey is to hold talent back inside the company. It exploits the notion of “” to define a digital representation of the talent, namely , built on the basis of skills level and personal traits. Given a target company’s role, Adaptive Talent Journey proposes the most suitable path of work experiences (journey) to improve the skills of a talent so to achieve the target role requirements. Such a mechanism resonates with the Reinforcement Learning paradigm, and specifically with . Specifically, the proposed method exploits: two double Deep Q-Networks (DDQNs) for selecting the work experiences to be made; a to support the DDQNs training and ensure good performance despite the limited availability of data.We implemented and deployed Adaptive Talent Journey in an intuitive Web application, namely