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

Edité par LAP LAMBERT Academic Publishing, 2025

In. N° de réf. du vendeur ria9786208434175_new

Synopsis :

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.

Détails bibliographiques

Titre : Reinforcement Learning in Robotics and ...
Éditeur : LAP LAMBERT Academic Publishing
Date d'édition : 2025
Reliure : Couverture souple
Etat : New