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Anotace knihy

The book is written from the perspective of the optimal feedback control of dynamic systems that evolve in either continuous- or discrete-time domains with emphasis on deterministic problem formulations over the corresponding stochastic problem formulations. Bellman's dynamic programming-optimal feedback control-in continuous- and discrete-time domains forms the mathematical foundations of this book. In a simple and clear manner, this book relates the relation of one of the main techniques of the reinforcement learning approach in computer science, so-called Q-learning, to the Bellman dynamic programming functional difference equation.

Reinforcement Learning for Engineering contains several exercises, homework problems, and design projects (most using MATLAB® and its Reinforcement Learning toolbox Simulink®; and some using Python) for real physical engineering systems. The book is a valuable reference for all researchers and practitioners interested in an engineering approach to reinforcement learning because it covers many essential results in a systematic manner. The book also presents and defines several future interesting and challenging research problems by providing a deeper physical and mathematical understanding of the optimal control Hamiltonians from the reinforcement learning point of view.

Parametry knihy

• Plný název: Reinforcement Learning for Engineering
• Podnázev: DE
• Autor: Zoran Gajic, Lingyi Xu
• Edice: Communications and Control Engineering,
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 330
• EAN: 9783032404015
• ID: 53616986
• Nakladatelství: Springer, Berlin
• Rozměry: 235 × 155 mm
• Datum vydání: 30. November 2026

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