MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02198nam 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 |
9781838644147 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
NULRC |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA 76.73.P98 .B55 2020 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bilgin, Enes |
| Relator term |
author |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Mastering reinforcement learning with python : |
| Remainder of title |
build next-generation, self-learning models using reinforcement learning techniques and best practices / |
| Statement of responsibility, etc. |
Enes Bilgin |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Birmingham, UK : |
| Name of publisher, distributor, etc. |
Packt Publishing, Limited, |
| Date of publication, distribution, etc. |
c2020 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 520 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
USD47 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Introduction to Reinforcement Learning -- Multi-armed Bandits -- Contextual Bandits -- Makings of the Markov Decision Process -- Solving the Reinforcement Learning Problem -- Deep Q-Learning at Scale -- Policy Based Methods -- Model-Based Methods -- Multi-Agent Reinforcement Learning -- Machine Teaching -- Generalization and Domain Randomization -- Meta-reinforcement learning -- Other Advanced Topics -- Autonomous Systems -- Supply Chain Management -- Marketing, Personalization and Finance -- Smart City and Cybersecurity -- Challenges and Future Directions in Reinforcement Learning. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Key Features Understand how large-scale state-of-the-art RL algorithms and approaches work Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more Explore tips and best practices from experts that will enable you to overcome real-world RL challenges Book Description Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
REINFORCEMENT LEARNING |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Books |
