Reinforcement Learning in Robotics and Autonomous Systems The State of the Art. DE
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- Englisch ausgewählt
Fr. 81.90
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
05.03.2025
Verlag
LAP LAMBERT Academic PublishingSeitenzahl
100
Maße (L/B/H)
22/15/0.7 cm
Gewicht
167 g
Sprache
Englisch
ISBN
978-620-8-43417-5
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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