Reinforcement Learning for Engineering
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Beschreibung
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.
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.
A structured treatment of significant results in the uses of reinforcement learning in control and engineering Exercises, homework problems and design projects implemented in MATLAB® and Python Presents several challenging open questions for future research
Autor*in
Zoran Gajić
Themen in »Reinforcement Learning for Engineering«
Reinforcement Learning Dynamic Programming Approximate Dynamic Programming Optimal Control Policy Iterations Value Iterations Zero-sum Differential Games Nash Differential Games Partial Model-free Reinforcement Learning Model-free Reinforcement Learning Markov Decision Processes Q-Learning