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003 - CONTROL NUMBER IDENTIFIER |
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005 - DATE AND TIME OF LATEST TRANSACTION |
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20140310143344.0 |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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100709s2010 gw | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9783642139321 |
|
978-3-642-13932-1 |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
006.3 |
Edition number |
23 |
264 #1 - |
-- |
Berlin, Heidelberg : |
-- |
Springer Berlin Heidelberg, |
-- |
2010. |
912 ## - |
-- |
ZDB-2-ENG |
100 1# - MAIN ENTRY--PERSONAL NAME |
Personal name |
Whiteson, Shimon. |
Relator term |
author. |
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
Title |
Adaptive Representations for Reinforcement Learning |
Medium |
[electronic resource] / |
Statement of responsibility, etc |
by Shimon Whiteson. |
300 ## - PHYSICAL DESCRIPTION |
Extent |
133p. 11 illus. in color. |
Other physical details |
online resource. |
440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE |
Title |
Studies in Computational Intelligence, |
International Standard Serial Number |
1860-949X ; |
Volume number/sequential designation |
291 |
505 0# - FORMATTED CONTENTS NOTE |
Formatted contents note |
Part 1 Introduction -- Part 2 Reinforcement Learning -- Part 3 On-Line Evolutionary Computation -- Part 4 Evolutionary Function Approximation -- Part 5 Sample-Efficient Evolutionary Function Approximation -- Part 6 Automatic Feature Selection for Reinforcement Learning -- Part 7 Adaptive Tile Coding -- Part 8 RelatedWork -- Part 9 Conclusion -- Part 10 Statistical Significance. |
520 ## - SUMMARY, ETC. |
Summary, etc |
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. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Engineering. |
|
Topical term or geographic name as entry element |
Artificial intelligence. |
|
Topical term or geographic name as entry element |
Engineering. |
|
Topical term or geographic name as entry element |
Computational Intelligence. |
|
Topical term or geographic name as entry element |
Artificial Intelligence (incl. Robotics). |
710 2# - ADDED ENTRY--CORPORATE NAME |
Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
773 0# - HOST ITEM ENTRY |
Title |
Springer eBooks |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Display text |
Printed edition: |
International Standard Book Number |
9783642139314 |
856 40 - ELECTRONIC LOCATION AND ACCESS |
Uniform Resource Identifier |
http://dx.doi.org/10.1007/978-3-642-13932-1 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
|
Item type |
E-Book |