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001 - NÚMERO DE CONTROL |
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u374623 |
003 - IDENTIFICADOR DEL NÚMERO DE CONTROL |
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SIRSI |
005 - FECHA Y HORA DE LA ULTIMA TRANSACCIÓN |
control field |
20160812084243.0 |
007 - CAMPO FIJO DE DESCRIPCIÓN FIJA--INFORMACIÓN GENERAL |
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008 - ELEMENTOS DE LONGITUD FIJA -- INFORMACIÓN GENERAL |
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100709s2010 gw | s |||| 0|eng d |
020 ## - NÚMERO INTERNACIONAL NORMALIZADO PARA LIBROS |
International Standard Book Number |
9783642139321 |
-- |
978-3-642-13932-1 |
040 ## - FUENTE DE CATALOGACIÓN |
Transcribing agency |
MX-MeUAM |
050 #4 - SIGNATURA TOPOGRÁFICA DE LA BIBLIOTECA DEL CONGRESO |
Classification number |
Q342 |
082 04 - NÚMERO DE CLASIFICACIÓN DECIMAL DEWEY |
Classification number |
006.3 |
Edition number |
23 |
100 1# - ASIENTO PRINCIPAL--NOMBRE PERSONAL |
Personal name |
Whiteson, Shimon. |
Relator term |
author. |
245 10 - MENCIÓN DE TITULO |
Title |
Adaptive Representations for Reinforcement Learning |
Medium |
[recurso electrónico] / |
Statement of responsibility, etc. |
by Shimon Whiteson. |
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
Place of production, publication, distribution, manufacture |
Berlin, Heidelberg : |
Name of producer, publisher, distributor, manufacturer |
Springer Berlin Heidelberg, |
Date of production, publication, distribution, manufacture, or copyright notice |
2010. |
300 ## - DESCRIPCIÓN FÍSICA |
Extent |
XIII, 116 p. |
Other physical details |
online resource. |
336 ## - CONTENT TYPE |
Content type term |
text |
Content type code |
txt |
Source |
rdacontent |
337 ## - MEDIA TYPE |
Media type term |
computer |
Media type code |
c |
Source |
rdamedia |
338 ## - CARRIER TYPE |
Carrier type term |
online resource |
Carrier type code |
cr |
Source |
rdacarrier |
347 ## - DIGITAL FILE CHARACTERISTICS |
File type |
text file |
Encoding format |
PDF |
Source |
rda |
490 1# - MENCIÓN DE SERIE |
Series statement |
Studies in Computational Intelligence, |
International Standard Serial Number |
1860-949X ; |
Volume/sequential designation |
291 |
505 0# - NOTA DE CONTENIDO |
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 ## - NOTA DE RESUMEN, 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. |
596 ## - |
-- |
19 |
650 #0 - ASIENTO SECUNDARIO DE MATERIA - TERMINO TEMÁTICO |
Topical term or geographic name as entry element |
Engineering. |
650 #0 - ASIENTO SECUNDARIO DE MATERIA - TERMINO TEMÁTICO |
Topical term or geographic name as entry element |
Artificial intelligence. |
650 14 - ASIENTO SECUNDARIO DE MATERIA - TERMINO TEMÁTICO |
Topical term or geographic name as entry element |
Engineering. |
650 24 - ASIENTO SECUNDARIO DE MATERIA - TERMINO TEMÁTICO |
Topical term or geographic name as entry element |
Computational Intelligence. |
650 24 - ASIENTO SECUNDARIO DE MATERIA - TERMINO TEMÁTICO |
Topical term or geographic name as entry element |
Artificial Intelligence (incl. Robotics). |
710 2# - ASIENTO SECUNDARIO - NOMBRE CORPORATIVO |
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 |
Relationship information |
Printed edition: |
International Standard Book Number |
9783642139314 |
830 #0 - ASIENTO SECUNDARIO DE SERIE--TITULO UNIFORME |
Uniform title |
Studies in Computational Intelligence, |
International Standard Serial Number |
1860-949X ; |
Volume number/sequential designation |
291 |
856 40 - LOCALIZACIÓN Y ACCESO ELECTRÓNICOS |
Public note |
Libro electrónico |
Uniform Resource Identifier |
<a href="http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-13932-1">http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-13932-1</a> |
942 ## - TIPO DE MATERIAL (KOHA) |
Koha item type |
Libro Electrónico |