Deep reinforcement learning hands-on : (Record no. 20225)

MARC details
000 -LEADER
fixed length control field 01909nam a2200229Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field NULRC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250520102954.0
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 9781788834247
040 ## - CATALOGING SOURCE
Transcribing agency NULRC
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q 325.5 .L37 2018
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Lapan, Maxim
Relator term author
245 #0 - TITLE STATEMENT
Title Deep reinforcement learning hands-on :
Remainder of title apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more /
Statement of responsibility, etc. Maxim Lapan
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Birmingham, UK :
Name of publisher, distributor, etc. Packt Publishing, Limited,
Date of publication, distribution, etc. c2018
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 523 pages ;
Dimensions 24 cm.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes index.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note What is Reinforcement Learning? -- OpenAI GymDeep Learning with PyTorch -- The Cross-Entropy Method -- Tabular Learning and the Bellman Equation -- Deep Q-Networks -- DQN Extensions -- Stocks Trading Using RL -- Policy Gradients - An Alternative -- The Actor-Critic Method -- Asynchronous Advantage Actor-Critic --Chatbots Training with RL Web Navigation -- Continuous Action Space -- Trust Regions - TRPO, PPO, and ACKTR -- Black-Box Optimization in RL -- Beyond Model-Free - Imagination -- AlphaGo Zero
520 ## - SUMMARY, ETC.
Summary, etc. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element REINFORCEMENT LEARNING
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Lapan, Maxim
Relator term co-author
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 09/08/2020 Purchased - Amazon 35.99   GC Q 325.5 .L37 2018 NULIB000017984 05/20/2025 c.1 05/20/2025 Books