ISBN: 9786208434175 - reinforcement learning in robotics and autonomous systems: the state of the art (14 resultados)

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Taschenbuch. Condición: Neu. Reinforcement Learning in Robotics and Autonomous Systems | The State of the Art | N. S. Usha (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208434175 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 100 pp. Englisch.

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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. …

Taschenbuch. Condición: 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. …