Algorithms for Reinforcement Learning: Synthesis Lectures on Artificial Intelligence and Machine Learning (Record no. 51670)

MARC details
000 -LEADER
fixed length control field 01815 a2200181 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250916152918.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031004230
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 SZE
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Szepesvari, Csaba
245 ## - TITLE STATEMENT
Title Algorithms for Reinforcement Learning: Synthesis Lectures on Artificial Intelligence and Machine Learning
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Springer Nature
Place of publication, distribution, etc Switzerland
Date of publication, distribution, etc 2022
300 ## - PHYSICAL DESCRIPTION
Extent 89
520 ## - SUMMARY, ETC.
Summary, etc Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification

No items available.