Multi-agent coordination : (Record no. 69394)

000 -LEADER
fixed length control field 03183cam a22005538i 4500
001 - CONTROL NUMBER
control field on1158507353
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220711203627.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200602s2021 nju ob 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119699057
-- (electronic bk. : oBook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119699053
-- (electronic bk. : oBook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119699026
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119699029
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119698999
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119698995
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (cloth)
029 1# - (OCLC)
OCLC library identifier AU@
System control number 000067267634
082 00 - CLASSIFICATION NUMBER
Call Number 006.3/1
100 1# - AUTHOR NAME
Author Sadhu, Arup Kumar,
245 10 - TITLE STATEMENT
Title Multi-agent coordination :
Sub Title a reinforcement learning approach /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource
520 ## - SUMMARY, ETC.
Summary, etc "This book explores the usage of Reinforcement Learning for Multi-Agent Coordination. Chapter 1 introduces fundamentals of the multi-robot coordination. Chapter 2 offers two useful properties, which have been developed to speed-up the convergence of traditional multi-agent Q-learning (MAQL) algorithms in view of the team-goal exploration, where team-goal exploration refers to simultaneous exploration of individual goals. Chapter 3 proposes the novel consensus Q-learning (CoQL), which addresses the equilibrium selection problem. Chapter 4 introduces a new dimension in the literature of the traditional correlated Q-learning (CQL), in which correlated equilibrium (CE) is computed partly in the learning and the rest in the planning phases, thereby requiring CE computation once only. Chapter 5 proposes an alternative solution to the multi-agent planning problem using meta-heuristic optimization algorithms. Chapter 6 provides the concluding remarks based on the principles and experimental results acquired in the previous chapters. Possible future directions of research are also examined briefly at the end of the chapter."--
700 1# - AUTHOR 2
Author 2 Konar, Amit,
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1002/9781119699057
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Hoboken, New Jersey :
-- Wiley-IEEE,
-- [2021]
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- n
-- rdamedia
338 ## -
-- online resource
-- nc
-- rdacarrier
520 ## - SUMMARY, ETC.
-- Provided by publisher.
588 ## -
-- Description based on print version record and CIP data provided by publisher; resource not viewed.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Reinforcement learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Multiagent systems.
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Multiagent systems
-- (OCoLC)fst01749717
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Reinforcement learning
-- (OCoLC)fst01732553
994 ## -
-- 92
-- DG1

No items available.