Reinforcement Learning : (Record no. 6600)

MARC details
000 -LEADER
fixed length control field 02104 a2200217 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251010094945.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 251010b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780262039246
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number SutR
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Sutton, Richard S.
245 ## - TITLE STATEMENT
Title Reinforcement Learning :
Remainder of title An Introduction /
Statement of responsibility, etc Richard S. Sutton & Andrew G. Barto
250 ## - EDITION STATEMENT
Edition statement 2nd ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher MIT Press :
Place of publication Cambridge ,
Year of publication ©2018.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xxii,526p.
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Adaptive Computation and Machine Learning :
520 ## - SUMMARY, ETC.
Summary, etc 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. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.<br/><br/>Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Reinforcement learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Dynamic Progamming
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Barto, Andrew G.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Collection code Home library Current library Shelving location Date acquired Source of acquisition Purchase Price Bill number Full call number Accession Number Print Price Bill Date/Price effective from Koha item type
      Humanities Indian Institute of Technology Tirupati Indian Institute of Technology Tirupati General Stacks 07/10/2025 Shah Book House 6132.70 SBH/28599 006.31 SutR (12179) 12179 8761.00 07/10/2025 Books