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REINFORCEMENT LEARNING-DRIVEN AUTONOMOUS SYSTEMS FOR SMART ROBOTICS AND INDUSTRIAL AUTOMATION

Dr. R. Sundar, Dr. Vipin Vijayan, Dr. Mohamed Abbas S, Dr. P. Mahalakshmi

Indexed In: Google scholar

Release Date: To be updated | Copyright:©2026 | Pages: 500

DOI: To be updated Cite

ISBN10: 0 | ISBN13: 0

Reinforcement Learning-Driven Autonomous Systems for Smart Robotics and Industrial Automation explores the principles, algorithms, and applications of reinforcement learning in developing intelligent autonomous systems. The book covers fundamental concepts, mathematical foundations, deep reinforcement learning techniques, multi-agent learning, and adaptive decision-making strategies. It highlights the integration of reinforcement learning with robotics, industrial automation, smart manufacturing, and cyber-physical systems. Real-world applications such as robotic navigation, predictive maintenance, process optimization, and intelligent control are discussed. The book also addresses challenges including safety, scalability, explainability, and real-time deployment, making it a valuable reference for researchers, students, and industry professionals.

Reinforcement Learning-Driven Autonomous Systems for Smart Robotics and Industrial Automation covers the theoretical foundations, advanced methodologies, and practical applications of reinforcement learning for intelligent autonomous systems. The book includes artificial intelligence learning paradigms, reinforcement learning fundamentals, agent–environment interaction, states, actions, rewards, policies, value functions, and Markov Decision Processes. It explores value-based learning, policy optimization, deep reinforcement learning, multi-agent reinforcement learning, simulation environments, transfer learning, and safety-aware learning approaches. The coverage extends to autonomous robotics, smart manufacturing, industrial automation, predictive maintenance, robotic control, sensor integration, and real-time decision-making. Emerging challenges and future directions in intelligent autonomous systems are also discussed.

Table Of Contents

Detailed Table Of Contents


Chapter 1

FOUNDATIONS OF REINFORCEMENT LEARNING FOR AUTONOMOUS ROBOTICS AND INDUSTRIAL SYSTEMS

Janani Rajaraman, Amit Kumar Bhakta, A. Suresh

(Pages:39)

Chapter 2

MARKOV DECISION PROCESSES FOR MODELING SEQUENTIAL DECISION MAKING IN ROBOTICS

Rajan Singh, Nidhi Tiwari, B. Arun

(Pages:37)

Chapter 3

POLICY OPTIMIZATION TECHNIQUES FOR EFFICIENT REINFORCEMENT LEARNING IN AUTONOMOUS SYSTEMS

Brajesh Kumar Singh, Manikandan R, A. Suresh

(Pages:35)

Chapter 4

VALUE BASED METHODS FOR DECISION MAKING IN INDUSTRIAL ROBOTIC ENVIRONMENTS

S. Suresh Kannan, Ratnesh Kumar Gupta, A. Suresh

(Pages:33)

Chapter 5

MODEL BASED REINFORCEMENT LEARNING FOR ADAPTIVE CONTROL IN SMART ROBOTICS

Rajinder Singh, B. Parvathi Sangeetha, Kruthika V T

(Pages:31)

Chapter 6

DEEP REINFORCEMENT LEARNING ARCHITECTURES FOR COMPLEX INDUSTRIAL AUTOMATION TASKS

M. Sivaranjani, K. Sudha Devi, B. Parvathi Sangeetha

(Pages:38)

Chapter 7

EXPLORATION AND EXPLOITATION STRATEGIES IN REINFORCEMENT LEARNING FOR ROBOTICS

Kavita Sanjay Singh, Nikitha M kurian, B. Parvathi sangeetha

(Pages:36)

Chapter 8

MULTI AGENT REINFORCEMENT LEARNING FOR COORDINATED INDUSTRIAL ROBOTIC SYSTEMS

Sanjay Singh, K.Bharathi, P. Sumathi

(Pages:34)

Chapter 9

SIMULATION ENVIRONMENTS FOR TRAINING REINFORCEMENT LEARNING DRIVEN ROBOTIC SYSTEMS

Balraj Hooda, P Daniel Jeyakumar, K.Bharathi

(Pages:34)

Chapter 10

TRANSFER LEARNING TECHNIQUES FOR ACCELERATING REINFORCEMENT LEARNING IN ROBOTICS

V. Anitha, Sridhar, K. Manikandan

(Pages:32)

Chapter 11

SAFETY CONSTRAINTS AND RISK AWARE REINFORCEMENT LEARNING IN INDUSTRIAL AUTOMATION

Suberiya Begum S, Amit Kumar Bhakta, Mani Vannan M

(Pages:35)

Chapter 12

REAL TIME DECISION MAKING USING REINFORCEMENT LEARNING IN AUTONOMOUS ROBOTICS

Balasubramanian. T, S Satish Kumar, M. Yuvaraj

(Pages:39)

Chapter 13

REWARD DESIGN STRATEGIES FOR OPTIMIZING INDUSTRIAL ROBOTIC TASK PERFORMANCE

Kavita Sanjay Singh, Mani Vannan M, A. Suresh

(Pages:33)

Chapter 14

INTEGRATION OF REINFORCEMENT LEARNING WITH SENSOR DATA IN SMART ROBOTICS

R. Sundar, Riddhi Garg, G. Rohini

(Pages:37)

Chapter 15

DEPLOYMENT OF REINFORCEMENT LEARNING MODELS IN INDUSTRIAL AUTOMATION WORKFLOWS

Pawan Kumar Shukla, L. Sujitha, A. Thanikasalam

(Pages:38)


Contributions


Dr. R. Sundar is currently working as an Associate Professor in the Marine Engineering at AMET Deemed to be University. He received his Doctor of Philosophy (Ph.D.) in Engineering and Technology, specializing in Renewable Energy from AMET University. Dr.R.Sundar has 22 years of teaching experience, with areas of specialization that include Marine Automation and Control Systems, Marine Electrical Technology, Electrical Machines, Renewable Energy Systems, Automation, IoT and Power Electronics. He has published more than 32 research papers in reputed journals, 37 papers at various international conferences. He is also the author of the textbook Design of Electrical Machines. Digital Image processing, Control System Engineering, Electric and Hybrid Vehicle.

Dr. Vipin Vijayan is an Assistant Professor of Mechanical Engineering specializing in polymer mechanics, fracture mechanics, and plastic piping integrity. He earned his Ph.D. from Hannam University, South Korea, leading national projects on rapid crack propagation in polymers. With over ten years across India and South Korea, he teaches, researches, and develops laboratories. Proficient in FEM, CAD, ISO/ASTM/EN standards, and data acquisition, he has published in Scopus-indexed journals, delivered invited talks, and advanced methods for polymer joint testing. He mentors students in design, simulation, and electric vehicle projects, integrating research into teaching to inspire future engineers.

Dr. Mohamed Abbas S is a distinguished academician, innovator, and researcher with over 22 years of teaching, research, and industrial expertise. He serves as Professor & Head, Centre for Innovation and Incubation at PERI Institute of Technology, Chennai. Holding a Ph.D. in Mechanical Engineering from CEG, Anna University, his interests include IC Engines, Composites, Tribology, EV Technology, IoT, and Robotics. A Ph.D. supervisor at Anna University, he has 30+ international publications, 70+ patents, authored many books, delivered 300+ lectures, and produced many innovations/Products. Recognized with many prestigious awards, Dr. Abbas is also an editor, reviewer, and keynote speaker in global academia and industry.

Dr. P. Mahalakshmi received the Ph.D. degree in Information and Communication engineering and the specific areas of research include Optical communication using Metamaterial and Photonic crystal fiber structures. She is currently working as Professor in Sethu Institute of technology, INDIA and her Research works include sensor design for various applications like health monitoring and communication. She has published around 17 articles in journals from well reputed publishers including, Springer, Wiley and Elsevier. As a reviewer, she has reviewed articles from various journals, including JOC and Alexandria. She has got reviewer appreciation from Elsevier.

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