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Reinforcement Learning and Dynamic Programming Using Function Approximators (Hardcover, New)
Loot Price: R3,505
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Reinforcement Learning and Dynamic Programming Using Function Approximators (Hardcover, New)
Series: Automation and Control Engineering
Expected to ship within 10 - 15 working days
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From household appliances to applications in robotics, engineered
systems involving complex dynamics can only be as effective as the
algorithms that control them. While Dynamic Programming (DP) has
provided researchers with a way to optimally solve decision and
control problems involving complex dynamic systems, its practical
value was limited by algorithms that lacked the capacity to scale
up to realistic problems. However, in recent years, dramatic
developments in Reinforcement Learning (RL), the model-free
counterpart of DP, changed our understanding of what is possible.
Those developments led to the creation of reliable methods that can
be applied even when a mathematical model of the system is
unavailable, allowing researchers to solve challenging control
problems in engineering, as well as in a variety of other
disciplines, including economics, medicine, and artificial
intelligence. Reinforcement Learning and Dynamic Programming Using
Function Approximators provides a comprehensive and unparalleled
exploration of the field of RL and DP. With a focus on
continuous-variable problems, this seminal text details essential
developments that have substantially altered the field over the
past decade. In its pages, pioneering experts provide a concise
introduction to classical RL and DP, followed by an extensive
presentation of the state-of-the-art and novel methods in RL and DP
with approximation. Combining algorithm development with
theoretical guarantees, they elaborate on their work with
illustrative examples and insightful comparisons. Three individual
chapters are dedicated to representative algorithms from each of
the major classes of techniques: value iteration, policy iteration,
and policy search. The features and performance of these algorithms
are highlighted in extensive experimental studies on a range of
control applications. The recent development of applications
involving complex systems has led to a surge of interest in RL and
DP methods and the subsequent need for a quality resource on the
subject. For graduate students and others new to the field, this
book offers a thorough introduction to both the basics and emerging
methods. And for those researchers and practitioners working in the
fields of optimal and adaptive control, machine learning,
artificial intelligence, and operations research, this resource
offers a combination of practical algorithms, theoretical analysis,
and comprehensive examples that they will be able to adapt and
apply to their own work. Access the authors' website at
www.dcsc.tudelft.nl/rlbook/ for additional material, including
computer code used in the studies and information concerning new
developments.
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