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Books > Professional & Technical > Energy technology & engineering > Electrical engineering
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Learning-Based Control - A Tutorial and Some Recent Results (Paperback)
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Learning-Based Control - A Tutorial and Some Recent Results (Paperback)
Series: Foundations and Trends (R) in Systems and Control
Expected to ship within 10 - 15 working days
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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.
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