Adaptive Training Program Optimization in Human Resource Development Using Reinforcement Learning and Multilayer Perceptron Models

The investigation of sophisticated machine learning algorithms has been prompted by the growing need for individualized and efficient training programs in human resource development (HRD). This research leverages HR analytics from the Employee Promotion Data dataset on Kaggle to offer an adaptive training program optimization approach combining reinforcement learning (RL) techniques and Multilayer Perceptron (MLP) models. Developing a system that continually adapts training programs in response to the specific demands of each employee and anticipated results is the aim in order to improve worker satisfaction and productivity as a whole. Through the use of reinforcement learning, an adaptive feedback loop is created in which the training program changes in response to parameters measuring employee engagement and performance. Through the recommendation of individualized training modules, the RL agent gains knowledge on how to optimize long-term benefits, like skill acquisition and promotion preparedness. At the same time, MLP models are used to forecast the possibility of employee promotions based on past data, which is a crucial decision-making input for the RL agent. The dataset is pre-processed and input into the models to find important patterns and predictors of effective instruction outcomes. Features in the dataset include employee demographics, performance scores, and prior training. A strong method for streamlining training routes and guaranteeing that every worker gets the most pertinent and significant growth opportunities is offered by the combination of RL and MLP models. With an accuracy of 96.20%, the suggested framework proved to be highly successful in forecasting staff promotions and streamlining training initiatives. The suggested framework fosters a more competent and promotable workforce, which not only supports individual career progress but also aligns with organizational goals.