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Studies in Computational Intelligence

Adaptive Representations for Reinforcement Learning

Authors: Whiteson, Simon

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  • Covers recent research in adaptive representations for reinforcement learning
  • Written by leading experts in this field
  • Provides readers with the state of the art on this topic
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  • ISBN 978-3-642-13932-1
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About this book

This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.

Table of contents (9 chapters)

Table of contents (9 chapters)

Buy this book

eBook £79.50
price for United Kingdom (gross)
  • ISBN 978-3-642-13932-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover £109.99
price for United Kingdom (gross)
Softcover £99.99
price for United Kingdom (gross)
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Bibliographic Information

Bibliographic Information
Book Title
Adaptive Representations for Reinforcement Learning
Authors
Series Title
Studies in Computational Intelligence
Series Volume
291
Copyright
2010
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer Berlin Heidelberg
eBook ISBN
978-3-642-13932-1
DOI
10.1007/978-3-642-13932-1
Hardcover ISBN
978-3-642-13931-4
Softcover ISBN
978-3-642-42231-7
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XIII, 116
Topics

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