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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.
Produkteigenschaften
- Artikelnummer: 9783032404015
- Medium: Buch
- ISBN: 978-3-032-40401-5
- Verlag: Springer
- Erscheinungstermin: 30.01.2027
- Sprache(n): Englisch
- Auflage: Erscheinungsjahr 2027
- Serie: Communications and Control Engineering
- Produktform: Gebunden
- Seiten: 261
- Format (B x H): 155 x 235 mm
- Ausgabetyp: Kein, Unbekannt
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