Reinforcement Learning Applications in Autonomous Systems: From Traffic Optimization to Robotics
Authors:-Asha Devi
Abstract-:Reinforcement Learning (RL), a dynamic branch of machine learning, has emerged as a powerful tool for enabling autonomous decision-making in complex and uncertain environments. By learning through interaction, trial, and reward-based feedback, RL equips agents to optimize their actions without requiring explicit programming. This review explores the expanding role of RL across diverse autonomous systems, including traffic management, autonomous vehicles, industrial robotics, unmanned aerial vehicles (UAVs), and healthcare robotics. In traffic optimization, RL adapts to real-time flow patterns, significantly reducing congestion. For autonomous vehicles, RL facilitates safe and efficient navigation, leveraging deep learning for real-time perception and control. Industrial robotics benefit from RL by enhancing adaptability in tasks such as assembly and material handling, while UAVs gain from RL’s ability to support complex aerial maneuvers and cooperative missions. In healthcare, RL contributes to the development of intelligent surgical and rehabilitation robots that learn from both simulation and human interaction. The integration of RL with technologies like deep learning, computer vision, and sensor fusion continues to enhance autonomy across domains. While challenges such as safety, sample efficiency, and sim-to-real transfer remain, ongoing research promises scalable, robust RL solutions. This article presents a comprehensive analysis of current applications and the future trajectory of reinforcement learning in autonomous systems.
