Book announcement: "Reinforcement Learning and Dynamic Programming Using Functions Approximators"
Lucian Busoniu
Dear ML list readers,
We are pleased to announce the recent release of our book:
"Reinforcement Learning and Dynamic Programming Using Functions
Approximators"
(Lucian Busoniu, Robert Babuska, Bart De Schutter, and Damien Ernst)
in the Automation and Control Engineering series of Taylor & Francis CRC
Press.
Book information:
Reinforcement learning (RL) can optimally solve decision and control
problems involving complex dynamic systems, without requiring a
mathematical model of the system. If a model is available, dynamic
programming (DP), the model-based counterpart of RL, can be used. RL and
DP are applicable in a variety of disciplines, including artificial
intelligence, automatic control, economics, and medicine. Recent years
have seen a surge of interest RL and DP using compact, approximate
representations of the solution, which enable algorithms to address
realistic problems.
This book provides an in-depth introduction to RL and DP with function
approximators, with a focus on continuous-variable control problems. A
concise description of classical RL and DP (Chapter 2) builds the
foundation for the remainder of the book. This is followed by an
extensive review of the state-of-the-art in RL and DP with
approximation, which combines algorithm development with theoretical
guarantees, illustrative numerical examples, and algorithm comparisons
(Chapter 3). Each of the final three chapters (4 to 6) is dedicated to a
representative algorithm from the authors' research. These three
algorithms respectively belong to the three major classes of methods:
approximate value iteration, approximate policy iteration, and
approximate policy search. The features and performance of these
algorithms are highlighted in comprehensive experimental studies on a
range of control applications.
Features:
* A concise introduction to the basics of RL and DP
* A detailed treatment of RL and DP with function approximators,
including theoretical results and illustrative examples
* A thorough treatment of policy search techniques
* Comprehensive experimental studies on a range of control problems,
including real-time control results
* An extensive, illustrative convergence and consistency analysis of an
approximate value iteration algorithm
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 book's website at http://www.dcsc.tudelft.nl/rlbook/ for
additional information, including computer code used in the experimental
studies, information about ordering the book, etc.
Hoping you will find this book useful,
Sincerely,
Lucian Busoniu, on the authors' behalf
--
________________________________________________________________
Lucian Busoniu
Delft Center for Systems and Control,Delft Univ.of Technology
E-mail: i.l.b...@tudelft.nl
Web: http://www.dcsc.tudelft.nl/~lbusoniu
Phone: +31 (0)15 27 88573 Fax: +31 (0)15 27 86679
Location: Mekelweg 2, 2628 CD Delft, NL Room: 8C-3-12