Reinforcement Learning and Dynamic Programming Using Function Approxim

Description

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... Read more

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. Read less

1 Introduction
The dynamic programming and reinforcement learning problem
Approximation in dynamic programming and reinforcement learning
About this book
2 An introduction to dynamic programming and reinforcement learning
Introduction
Markov decision processes
Value iteration
Policy iteration
Policy search
Summary and discussion
3 Dynamic programming and reinforcement learning in large and continuous
spaces
Introduction
The need for approximation in large and continuous spaces
Approximation architectures
Approximate value iteration
Approximate policy iteration
Finding value function approximators automatically
Approximate policy search
Comparison of approximate value iteration, policy iteration, and policy search
Summary and discussion
4 Approximate value iteration with a fuzzy representation
Introduction
Fuzzy Q-iteration
Analysis of fuzzy Q-iteration
Optimizing the membership functions
Experimental study
Summary and discussion
5 Approximate policy iteration for online learning and continuous-action control
Introduction
A recapitulation of least-squares policy iteration
Online least-squares policy iteration
Online LSPI with prior knowledge
LSPI with continuous-action, polynomial approximation
Experimental study
Summary and discussion
6 Approximate policy search with cross-entropy optimization of basis functions
Introduction
Cross-entropy optimization
Cross-entropy policy search
Experimental study
Summary and discussion
Appendix A Extremely randomized trees
Structure of the approximator
Building and using a tree
Appendix B The cross-entropy method
Rare-event simulation using the cross-entropy method
Cross-entropy optimization
Symbols and abbreviations
Bibliography
List of algorithms
Index

Author(s)

Biography

Robert Babuska, Lucian Busoniu, and Bart de Schutter are with the Delft University of Technology. Damien Ernst is with the University of Liege.

Reinforcement Learning and Dynamic Programming Using Function Approximators

Description

Table of Contents

Author(s)

Biography

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