000			03183cam a22005538i 4500
001			on1158507353
003			OCoLC
005			20220711203627.0
006			m o d
007			cr |||||||||||
008			200602s2021 nju ob 001 0 eng
010			_a 2020024707
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dDG1 _dOCLCO
020			_a9781119699057 _q(electronic bk. : oBook)
020			_a1119699053 _q(electronic bk. : oBook)
020			_a9781119699026 _q(epub)
020			_a1119699029 _q(epub)
020			_a9781119698999 _q(adobe pdf)
020			_a1119698995 _q(adobe pdf)
020			_z9781119699033 _q(cloth)
029	1		_aAU@ _b000067267634
035			_a(OCoLC)1158507353
042			_apcc
050	0	0	_aQ325.6
082	0	0	_a006.3/1 _223
049			_aMAIN
100	1		_aSadhu, Arup Kumar, _eauthor. _99426
245	1	0	_aMulti-agent coordination : _ba reinforcement learning approach / _cArup Kumar Sadhu, Amit Konar.
263			_a2008
264		1	_aHoboken, New Jersey : _bWiley-IEEE, _c[2021]
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_a"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."-- _cProvided by publisher.
588			_aDescription based on print version record and CIP data provided by publisher; resource not viewed.
590			_bWiley Frontlist Obook All English 2020
650		0	_aReinforcement learning. _99427
650		0	_aMultiagent systems. _94974
650		7	_aMultiagent systems _2fast _0(OCoLC)fst01749717 _94974
650		7	_aReinforcement learning _2fast _0(OCoLC)fst01732553 _99427
655		4	_aElectronic books. _93294
700	1		_aKonar, Amit, _eauthor. _99428
776	0	8	_iPrint version: _aSadhu, Arup Kumar. _tMulti-agent coordination _dHoboken, New Jersey : Wiley-IEEE, [2021] _z9781119699033 _w(DLC) 2020024706
856	4	0	_uhttps://doi.org/10.1002/9781119699057 _zWiley Online Library
942			_cEBK
994			_a92 _bDG1
999			_c69394 _d69394