A Survey on Deep Reinforcement Learning Applications in Autonomous Systems: Applications, Open Challenges, and Future Directions
Shruti Govinda, Bouziane Brik, Saad Harous
Abstract
Deep Reinforcement Learning (DRL) has become a fundamental element in advancing Autonomous Systems, significantly transforming fields like autonomous vehicles, robotics, and drones. This survey paper provides a comprehensive overview of the role of DRL in autonomous systems, focusing on recent advancements, applications, and challenges. Through a synthesis of existing literature and case studies, the paper elucidates key principles, methodologies, and implications of integrating DRL into autonomous systems. The systematic examination of selected papers reveals recurring patterns, emerging trends, and identifies gaps and opportunities for further research. By exploring the applications of DRL across different autonomous systems, commonalities, distinctions, and prevalent challenges are discussed, laying the groundwork for future advancements and practical implementations in this rapidly evolving field.