Strategic Entrepreneurship and Economic Development Using Deep Multi-Agent Reinforcement Learning Models

Entrepreneurship drives economic growth and innovation, affecting national and global economies. Despite the many company alternatives, there is no success formula. Future entrepreneurs must understand which factors cause success or failure. Innovation and entrepreneurship are becoming increasingly important across industries and scales, and methods to increase entrepreneurial results are needed. A organized approach to data preprocessing, feature extraction, and model training is presented here. After data preprocessing, selected samples were carefully inspected for accuracy. Latent Dirichlet Allocation (LDA) feature extraction identified key entrepreneurial qualities and factors. Deep Multi-Agent Reinforcement Learning (D-MARL), a revolutionary prediction method, was used to train the model with 92.37% accuracy, the D-MARL model outperformed DRL and SVM. This significant improvement shows the model's capacity to interpret complex entrepreneurial data. D-MARL's capacity to predict entrepreneurial success provides a robust method for identifying key success determinants, according to the study. This strategy helps improve entrepreneurial outcomes and foster economic growth through entrepreneurship.