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02110pam a2200445 i 4500
001
14053033
005
20190501173239.0
008
190404s2018 maua b 001 0 eng
010
|a
2018023826
020
|a
9780262039246
|q
hardcover
|q
alkaline paper
020
|a
0262039249
|q
hardcover
|q
alkaline paper
024
|a
15261240
035
|a
(NhCcYBP) 2018023826
035
|a
14053033
040
|a
DLC
|b
eng
|e
rda
|c
DLC
|d
OCLCO
|d
OCLCF
|d
NhCcYBP
042
|a
pcc
050
0
0
|a
Q325.6
|b
.R45 2018
079
|a
(OCoLC)1043175824
090
|a
Q325.6
|b
.R45 2018 (LC)
100
1
|a
Sutton, Richard S.,
|e
author.
245
1
0
|a
Reinforcement learning :
|b
an introduction /
|c
Richard S. Sutton and Andrew G. Barto.
250
|a
Second edition.
264
1
|a
Cambridge, Massachusetts :
|b
The MIT Press,
|c
[2018]
300
|a
xxii, 526 pages ;
|c
24 cm.
336
|a
text
|b
txt
|2
rdacontent
337
|a
unmediated
|b
n
|2
rdamedia
338
|a
volume
|b
nc
|2
rdacarrier
490
1
|a
Adaptive computation and machine learning
520
|a
"Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."--
|c
Provided by publisher.
504
|a
Includes bibliographical references and index.
650
0
|a
Reinforcement learning.
|0
http://id.loc.gov/authorities/subjects/sh92000704
650
7
|a
Reinforcement learning.
|2
fast
|0
(OCoLC)fst01732553
700
1
|a
Barto, Andrew G.,
|e
author.
830
0
|a
Adaptive computation and machine learning.
|0
http://id.loc.gov/authorities/names/n97066095
901
|a
Q325.6
902
|a
Sterling Memorial Library
|b
SML, Stacks, LC Classification >> Q325.6 .R45 2018 (LC)|DELIM|14061407
907
|a
2019-04-29T10:18:16.000Z
960
|a
39002134897124
|o
1
|s
71.42
|t
sml
|u
PRAGEM151
961
|c
190423
|f
5641
|m
653110
987
|c
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