Practical deep reinforcement learning with python / (Record no. 21786)

MARC details
000 -LEADER
fixed length control field 01926nam a2200229Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field NULRC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250520103029.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250520s9999 xx 000 0 und d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355512055
040 ## - CATALOGING SOURCE
Transcribing agency NULRC
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA 76.73.P98 .G75 2022
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Gridin, Ivan
Relator term author
245 #0 - TITLE STATEMENT
Title Practical deep reinforcement learning with python /
Statement of responsibility, etc. Ivan Gridin
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Delhi :
Name of publisher, distributor, etc. BPB Publications,
Date of publication, distribution, etc. c2022
300 ## - PHYSICAL DESCRIPTION
Extent xx, 377 pages :
Other physical details illustrations ;
Dimensions 24 cm.
365 ## - TRADE PRICE
Price amount USD30
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes index.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note 1. Introducing Reinforcement Learning -- 2. Playing Monopoly and Markov Decision Process -- 3. Training in Gym -- 4. Struggling with Multi- Armed Bandits -- 5. Blackjack in Monte Carlo -- 6. Escaping Maze with Q-Learning -- 7. Discretization -- Part II. Deep Reinforcement Learning -- 8. TensorFlow, PyTorch, and Your First Neural Network -- 9. Deep Q-Network and Lunar Lander -- 10. Defending Atlantis With Double Deep Q-Network -- 11. From Q-Learning to Policy-Gradient -- 12. Stock Trading With Actor-Critic -- 13. What Is Next?.
520 ## - SUMMARY, ETC.
Summary, etc. This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning. The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element PYTHON (COMPUTER PROGRAM LANGUAGE)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Library of Congress Classification     Machine Learning LRC - Main National University - Manila General Circulation 05/07/2024 Purchased - Amazon 30.00   GC QA 76.73.P98 .G75 2022 NULIB000019545 05/20/2025 c.1 05/20/2025 Books