Reinforcement Learning in Autonomous Systems: Advances, Applications, and Challenges

Abstract

Reinforcement Learning (RL) in autonomous systems has emerged as a transformative approach to enabling intelligent decision-making in dynamic and complex environments. Advances in RL algorithms, such as deep reinforcement learning and model-based methods, have significantly improved the ability of autonomous systems to learn from interactions and optimize their behavior over time. These advancements have led to a wide range of applications, including self-driving cars, robotics, unmanned aerial vehicles (UAVs), and adaptive control systems, where RL techniques are utilized to enhance autonomy, adaptability, and efficiency. However, the deployment of RL in real-world autonomous systems presents several challenges, including the need for massive computational resources, safety concerns, and the difficulty of generalizing learned behaviors across diverse scenarios. Addressing these challenges requires ongoing research in areas such as safe exploration, transfer learning, and the development of robust algorithms that can operate effectively in uncertain and dynamic environments. As RL continues to evolve, it’s potential to revolutionize autonomous systems remains immense, promising to drive further innovation and practical applications in the coming years.