This monograph describes the use of principles of reinforcement
learning (RL) to design feedback policies for continuous-time
dynamical systems that combine features of adaptive control and
optimal control. In a control engineering context, RL bridges the
gap between traditional optimal control and adaptive control
algorithms.The authors give an insightful introduction to
reinforcement learning techniques that can address various control
problems. In this context, they give a detailed description of
techniques such as Game-Theoretic Learning, Q-learning, and
Intermittent RL; with each chapter providing a self-contained
exposition of the topic and giving the reader suggestions for
further reading. Finally, the authors demonstrate the application
of the techniques in autonomous vehicles.This review of a topic
that is rapidly becoming ubiquitous in many engineering systems
enables to reader dip in and out of the topic to quickly understand
the essentials and provides the starting point for further
research.
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