Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Design of Experiments for Reinforcem...
~
SpringerLink (Online service)
Design of Experiments for Reinforcement Learning
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Design of Experiments for Reinforcement Learning/ by Christopher Gatti.
Author:
Gatti, Christopher.
Description:
XIII, 191 p. 46 illus., 25 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Logic design. -
Online resource:
https://doi.org/10.1007/978-3-319-12197-0
ISBN:
9783319121970
Design of Experiments for Reinforcement Learning
Gatti, Christopher.
Design of Experiments for Reinforcement Learning
[electronic resource] /by Christopher Gatti. - 1st ed. 2015. - XIII, 191 p. 46 illus., 25 illus. in color.online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053. - Springer Theses, Recognizing Outstanding Ph.D. Research,.
Introduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices.
This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.
ISBN: 9783319121970
Standard No.: 10.1007/978-3-319-12197-0doiSubjects--Topical Terms:
561473
Logic design.
LC Class. No.: Q342
Dewey Class. No.: 006.3
Design of Experiments for Reinforcement Learning
LDR
:02172nam a22004095i 4500
001
966605
003
DE-He213
005
20200701100944.0
007
cr nn 008mamaa
008
201211s2015 gw | s |||| 0|eng d
020
$a
9783319121970
$9
978-3-319-12197-0
024
7
$a
10.1007/978-3-319-12197-0
$2
doi
035
$a
978-3-319-12197-0
050
4
$a
Q342
072
7
$a
UYQ
$2
bicssc
072
7
$a
TEC009000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
100
1
$a
Gatti, Christopher.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1064173
245
1 0
$a
Design of Experiments for Reinforcement Learning
$h
[electronic resource] /
$c
by Christopher Gatti.
250
$a
1st ed. 2015.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2015.
300
$a
XIII, 191 p. 46 illus., 25 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Springer Theses, Recognizing Outstanding Ph.D. Research,
$x
2190-5053
505
0
$a
Introduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices.
520
$a
This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.
650
0
$a
Logic design.
$3
561473
650
0
$a
Computational intelligence.
$3
568984
650
0
$a
Artificial intelligence.
$3
559380
650
1 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Logic Design.
$3
670915
650
2 4
$a
Artificial Intelligence.
$3
646849
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319121987
776
0 8
$i
Printed edition:
$z
9783319121963
776
0 8
$i
Printed edition:
$z
9783319385518
830
0
$a
Springer Theses, Recognizing Outstanding Ph.D. Research,
$x
2190-5053
$3
1253569
856
4 0
$u
https://doi.org/10.1007/978-3-319-12197-0
912
$a
ZDB-2-ENG
912
$a
ZDB-2-SXE
950
$a
Engineering (SpringerNature-11647)
950
$a
Engineering (R0) (SpringerNature-43712)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login