Deep Reinforcement Learning in Robotics and Autonomous Systems

Abstract

Deep reinforcement learning (DRL) has begun as a powerful paradigm for enabling intelligent behavior in robotics and autonomous systems. A potential method for autonomously learning complicated behaviors from cursory sensor data is deep reinforcement learning (RL). Deep RL has shown potential in empowering somatic robots to acquire difficult abilities in the actual world despite a substantial chunk of research focusing on solicitations in audiovisual games and virtual control, which do not link with the restrictions of erudition in existent situations. Actual-world robotics, which is closely related to how individuals learn as personified agents in the actual world—provides an interesting arena for assessing such algorithms. Numerous difficulties arise while learning to see and move in the actual environment; some of these difficulties are simpler to solve than others, and some of these difficulties are frequently overlooked in RL research that solely considers virtual domains. In this chapter, we give many case revisions employing mechanical deep RL. We examine often-recognized deep RL issues and how these issues have been addressed in these studies, building on these case examples. We also give a brief summary of other problems that still need to be solved, numerous of which are specific to the real-world computing environment and are not frequently the subject of conventional RL research. This chapter provides a widespread outline of the solicitation of DRL techniques to address the challenges and complexities inherent in robotic tasks. The chapter also highlights the importance of transfer learning, sim-to-real transfer, and safety considerations in deploying DRL agents in real-world environments. Finally, we outline future directions and the ongoing challenges that researchers and practitioners face in harnessing the potential of DRL for shaping the future of robotics and autonomous systems.