Cloud-Based HR Management: Using Deep Reinforcement Learning to Optimize Employee Lifecycle

Cloud-Based HR Management is quickly becoming the new norm and a standard for current and future systems for the management of human resources in organizations. The applications of artificial intelligence in business reveal themselves when businesses start integrating such technologies in various processes to help them improve the engagement of their employees and also effectively deliver efficient outcomes. However, prior studies have had limitations in their capacity such as the fact that they generally used conventional machine learning methods which are inflexibly accurate. These limitations can lead to ineffective recruitment attempts and unattractive talent management, which requires more elaborate solutions. This work suggests a new approach based on the integration of a Deep Reinforcement Learning (DRL) model, operated in the cloud, to maximize the employee lifecycle. The DRL model shows excellent performance in terms of accuracy which is 99.1 %. The use of real-time data and abilities to alter contributions to the learning process makes the proposed method a step forward as compared to previous methods that limited the abilities of HR professionals to make sound decisions. The implementation of this model was done employing Python. The findings show that it does not only improve candidate selection but also the hiring process and overall performance of an organization. This research opens up the opportunity for subsequent research in extending DRL into other available HR functions to improve the effectiveness of Cloud based HR management systems. By adopting this advanced methodology the organizations, the human resource management working techniques can be enhanced and also the committed and effective employees.