"Whilst reinforcement learning has gained tremendous success and popularity in recent years, most research papers and books focus on either the theory (optimal control and dynamic programming) or the algorithms (mostly simulation-based). From a control systems perspective, this book will provide a model-based framework that bridges these two aspects to provide a holistic treatment of the topic of model-based online learning control. The aim is to develop a model-based framework for data-driven control that encompasses the ...
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"Whilst reinforcement learning has gained tremendous success and popularity in recent years, most research papers and books focus on either the theory (optimal control and dynamic programming) or the algorithms (mostly simulation-based). From a control systems perspective, this book will provide a model-based framework that bridges these two aspects to provide a holistic treatment of the topic of model-based online learning control. The aim is to develop a model-based framework for data-driven control that encompasses the topics of systems identification from data, model-based reinforcement learning and optimal control, and their applications. This will be done through reviewing the classical results in system identification from a new perspective to develop more efficient reinforcement learning techniques. Hence, the focus of this book will be on presenting an end to end framework from design to application of a more tractable model-based reinforcement learning technique. The tutorial aspects of the book are enhanced by the provision of a Python-based toolbox, accessible online"--
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