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Enhancing Robotic Autonomy and Deep Reinforcement Learning Applications
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Author(s): Ridhima Goel (Institute of Management Studies and Research, Maharshi Dayanand University, Rohtak, India)and Jagdeep Singla (Institute of Management Studies and Research, Maharshi Dayanand University, Rohtak, India)
Copyright: 2024
Pages: 18
Source title:
Robo-Advisors in Management
Source Author(s)/Editor(s): Swati Gupta (Chitkara University, India), Sanjay Taneja (Graphic Era University (Deemed), India), Vijay Kumar (Chaudhary Ranbir Singh University, India), Ercan Ozen (Usak University, Turkey)and Luan Vardari (University "Ukshin Hoti" Prizren, Kosovo)
DOI: 10.4018/979-8-3693-2849-1.ch020
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Abstract
The integration of Deep Reinforcement Learning (DRL) into the realm of robotics and autonomous systems has emerged as a groundbreaking paradigm shift, empowering machines to tackle intricate tasks through interaction with their environments. This chapter offers a comprehensive examination of the current research landscape at the intersection of DRL and robotics within this dynamic field. This chapter navigates through the conceptualization of DRL and explores its diverse applications in controlling robotics and object manipulation. The chapter showcases the autonomy and adaptability enabled by DRL while addressing prevalent challenges such as sample efficiency, safety concerns, and scalability. In conclusion, this chapter serves as a valuable resource for future researchers and practitioners intrigued by the intersection of DRL and robotics. It synthesizes current knowledge, underscores significant progress made, and maps out exciting avenues for further exploration, ultimately propelling the advancement of robotic systems in the era of machine learning and artificial intelligence.
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