Deep Reinforcement Learning for Autonomous System Optimization in Indonesia: A Systematic Literature Review - Yusuf, Dedi; Supraptono, Eko; Suryanto, Agus (Informatika, Universitas Jenderal Soedirman, 2025)
Background: The development of artificial intelligence (AI) technology, including Deep Reinforcement Learning (DRL), has brought significant changes in various industrial sectors, especially in autonomous systems. DRL combines the capabilities of Deep Learning (DL) in processing complex data with those of Reinforcement Learning (RL) in making adaptive decisions through interaction with the environment. However, the application of DRL in autonomous systems still faces several challenges, such as training stability, model generalization, and high data and computing resource requirements. Methods: This study uses the Systematic Literature Review (SLR) method to identify, evaluate, and analyze the latest developments in DRL for autonomous system optimization. The SLR was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, which consists of four main stages: identification, screening, eligibility, and inclusion of research articles. Data were collected through literature searches in leading scientific journal databases such as IEEE Xplore, MDPI, ACM Digital Library, ScienceDirect (Elsevier), SpringerLink, arXiv, Scopus, and Web of Science. Results: This study found that DRL has been widely adopted in various industrial sectors, including transportation, industrial robotics, and traffic management. The integration of DRL with other technologies such as Computer Vision, IoT, and Edge Computing further enhances its capability to handle uncertain and dynamic environments. Therefore, this study is crucial in providing a comprehensive understanding of the potential, challenges, and future directions of DRL development in autonomous systems, in order to foster more adaptive, efficient, and reliable technological innovations.