Reinforcement Learning for Sequential Decision and Optimal Control (Record no. 15192)

MARC details
000 -LEADER
fixed length control field 04060nam a2200385Ia 4500
000 - LEADER
fixed length control field 04793nam a22004215i 4500
001 - CONTROL NUMBER
control field 978-981-19-7784-8
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240319120854.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230405s2023 si | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789811977848
-- 978-981-19-7784-8
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 6.31
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Li, Shengbo Eben.
9 (RLIN) 31190
245 ## - TITLE STATEMENT
Title Reinforcement Learning for Sequential Decision and Optimal Control
Statement of responsibility, etc. by Shengbo Eben Li.
Medium [electronic resource] /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2023.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Singapore
Name of publisher, distributor, etc. Springer Nature Singapore
Date of publication, distribution, etc. 2023
300 ## - PHYSICAL DESCRIPTION
Extent XXX, 462 p. 217 illus., 213 illus. in color.
Other physical details online resource.
520 ## - SUMMARY, ETC.
Summary, etc. Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman's optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Automation.
9 (RLIN) 31191
Topical term or geographic name entry element Computational intelligence.
9 (RLIN) 31192
Topical term or geographic name entry element Computational Intelligence.
9 (RLIN) 31193
Topical term or geographic name entry element Control engineering.
9 (RLIN) 31194
Topical term or geographic name entry element Control theory.
9 (RLIN) 31195
Topical term or geographic name entry element Control, Robotics, Automation.
9 (RLIN) 31196
Topical term or geographic name entry element Engineering mathematics.
9 (RLIN) 31197
Topical term or geographic name entry element Engineering Mathematics.
9 (RLIN) 31198
Topical term or geographic name entry element Machine learning.
9 (RLIN) 31199
Topical term or geographic name entry element Machine Learning.
9 (RLIN) 31200
Topical term or geographic name entry element Robotics.
9 (RLIN) 31201
Topical term or geographic name entry element System theory.
9 (RLIN) 31202
Topical term or geographic name entry element Systems Theory, Control .
9 (RLIN) 31203
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-981-19-7784-8">https://doi.org/10.1007/978-981-19-7784-8</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type e-Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Total Checkouts Barcode Date last seen Price effective from Koha item type Public note
        S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Online   Veda Library Solutions Pvt. Ltd., Noida   EB2178 2024-03-19 2024-03-19 e-Book Platform:Springer