Reinforcement Learning in Robotics and Autonomous Systems: The State of the Art

N.S.Usha, Dr.; G.M.KarpuraDheepan, Dr.; N.Srinivasan, Dr.

ISBN 10: 6208434173 ISBN 13: 9786208434175

Editorial: LAP LAMBERT Academic Publishing, 2025

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Reinforcement Learning (RL) has emerged as a transformative approach in the field of autonomous systems, enabling intelligent decision making and control in robotics, self-driving cars, healthcare, industrial automation, and smart infrastructure. Throughout this discussion, we have explored the fundamental concepts, methodologies, challenges, and real world applications of RL in autonomous systems, highlighting both its potential and its limitations. The application of RL in robotics and autonomous systems is underpinned by Markov Decision Processes (MDPs), which provide a structured framework for sequential decision making. The development of value based methods, such as Deep Q Networks (DQN), and policy-based approaches, such as Policy Gradient and Actor Critic methods, has enabled robots and autonomous agents to learn complex behaviors through trial and error. Moreover, model free and model based RL techniques offer different trade offs in terms of sample efficiency and adaptability, paving the way for more versatile and practical learning based controllers.

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T�tulo: Reinforcement Learning in Robotics and ...
Editorial: LAP LAMBERT Academic Publishing
A�o de publicaci�n: 2025
Encuadernaci�n: Encuadernaci�n de tapa blanda
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Reinforcement Learning in Robotics and Autonomous Systems | The State of the Art

N. S. Usha (u. a.)

Publicado por LAP LAMBERT Academic Publishing, 2025

ISBN 10: 6208434173 ISBN 13: 9786208434175

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Reinforcement Learning in Robotics and Autonomous Systems

N.S.Usha, Dr.; G.M.KarpuraDheepan, Dr.; N.Srinivasan, Dr.

Publicado por LAP LAMBERT Academic Publishing, 2025

ISBN 10: 6208434173 ISBN 13: 9786208434175

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Reinforcement Learning in Robotics and Autonomous Systems (Paperback)

Dr N.S. Usha

Publicado por LAP Lambert Academic Publishing, 2025

ISBN 10: 6208434173 ISBN 13: 9786208434175

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Paperback. Condici�n: new. Paperback. Reinforcement Learning (RL) has emerged as a transformative approach in the field of autonomous systems, enabling intelligent decision making and control in robotics, self-driving cars, healthcare, industrial automation, and smart infrastructure. Throughout this discussion, we have explored the fundamental concepts, methodologies, challenges, and real world applications of RL in autonomous systems, highlighting both its potential and its limitations. The application of RL in robotics and autonomous systems is underpinned by Markov Decision Processes (MDPs), which provide a structured framework for sequential decision making. The development of value based methods, such as Deep Q Networks (DQN), and policy-based approaches, such as Policy Gradient and Actor Critic methods, has enabled robots and autonomous agents to learn complex behaviors through trial and error. Moreover, model free and model based RL techniques offer different trade offs in terms of sample efficiency and adaptability, paving the way for more versatile and practical learning based controllers. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de ref. del art�culo: 9786208434175

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Reinforcement Learning in Robotics and Autonomous Systems

N.S.Usha, Dr.; G.M.KarpuraDheepan, Dr.; N.Srinivasan, Dr.

Publicado por LAP LAMBERT Academic Publishing, 2025

ISBN 10: 6208434173 ISBN 13: 9786208434175

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Reinforcement Learning in Robotics and Autonomous Systems

N. S. Usha

Publicado por LAP LAMBERT Academic Publishing M�r 2025, 2025

ISBN 10: 6208434173 ISBN 13: 9786208434175

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Taschenbuch. Condici�n: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Reinforcement Learning (RL) has emerged as a transformative approach in the field of autonomous systems, enabling intelligent decision making and control in robotics, self-driving cars, healthcare, industrial automation, and smart infrastructure. Throughout this discussion, we have explored the fundamental concepts, methodologies, challenges, and real world applications of RL in autonomous systems, highlighting both its potential and its limitations. The application of RL in robotics and autonomous systems is underpinned by Markov Decision Processes (MDPs), which provide a structured framework for sequential decision making. The development of value based methods, such as Deep Q Networks (DQN), and policy-based approaches, such as Policy Gradient and Actor Critic methods, has enabled robots and autonomous agents to learn complex behaviors through trial and error. Moreover, model free and model based RL techniques offer different trade offs in terms of sample efficiency and adaptability, paving the way for more versatile and practical learning based controllers.Books on Demand GmbH, �berseering 33, 22297 Hamburg 100 pp. Englisch. N� de ref. del art�culo: 9786208434175

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Reinforcement Learning in Robotics and Autonomous Systems

N. S. Usha

Publicado por LAP LAMBERT Academic Publishing M�r 2025, 2025

ISBN 10: 6208434173 ISBN 13: 9786208434175

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Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch. N� de ref. del art�culo: 9786208434175

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