Deep Reinforcement Learn In Robotics Challenges and Applications

Abstract

Deep reinforcement learning (DRL) has emerged as a strong paradigm in the field of robotics, offering promising solutions to complex decision-making problems. This paper provides an overview of the challenges and applications of DRL in robotics. We discuss the basic concepts of DRL, how it integrates with robotic systems, and the challenges associated with this integration. We also explore various applications of DRL in robotics, highlighting the impact of this technology in areas such as automatic communication, flexibility, and control. By addressing these challenges and demonstrating real-world applications, this paper demonstrates the potential of DRL to shape robotic systems in the future.