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Learning-Based Control

A Tutorial and Some Recent Results

Zhong-Ping Jiang, Tao Bian, Weinan Gao
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Omschrijving

The recent success of Reinforcement Learning and related methods can be attributed to several key factors. First, it is driven by reward signals obtained through the interaction with the environment. Second, it is closely related to the human learning behavior. Third, it has a solid mathematical foundation. Nonetheless, conventional Reinforcement Learning theory exhibits some shortcomings particularly in a continuous environment or in considering the stability and robustness of the controlled process. In this monograph, the authors build on Reinforcement Learning to present a learning-based approach for controlling dynamical systems from real-time data and review some major developments in this relatively young field. In doing so the authors develop a framework for learning-based control theory that shows how to learn directly suboptimal controllers from input-output data. There are three main challenges on the development of learning-based control. First, there is a need to generalize existing recursive methods. Second, as a fundamental difference between learning-based control and Reinforcement Learning, stability and robustness are important issues that must be addressed for the safety-critical engineering systems such as self-driving cars. Third, data efficiency of Reinforcement Learning algorithms need be addressed for safety-critical engineering systems. This monograph provides the reader with an accessible primer on a new direction in control theory still in its infancy, namely Learning-Based Control Theory, that is closely tied to the literature of safe Reinforcement Learning and Adaptive Dynamic Programming.

Specificaties

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Auteur(s):
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Inhoud

Aantal bladzijden:
120
Taal:
Engels
Reeks:

Eigenschappen

Productcode (EAN):
9781680837520
Verschijningsdatum:
8/12/2020
Uitvoering:
Paperback
Formaat:
Trade paperback (VS)
Afmetingen:
156 mm x 234 mm
Gewicht:
181 g
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