000			01958cam a2200361 i 4500
001			18175924
005			20260412091429.0
008			140604t20142014njua b 001 0 eng
010			_a 2014016950
020			_a9781118362082 _q(hardback)
035			_a18175924
040			_aDLC _beng _cDLC _erda _dDLC _dAU
042			_apcc
049			_aAlfaisal Main Library
050	0	0	_aQ325.6 _b.S39 2014
100	1		_aSchwartz, Howard M., _eeditor.
245	1	0	_aMulti-agent machine learning : _ba reinforcement approach / _cHoward M. Schwartz, Department of Systems and Computer Engineering, Carleton University.
264		1	_aHoboken, New Jersey : _bWiley, _c©2014
300			_a242 pages _billustrations ; _c25 cm
336			_atext _2rdacontent _btxt
337			_aunmediated _2rdamedia _bn
338			_avolume _2rdacarrier _bnc
504			_aIncludes bibliographical references and index.
520			_a"Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of the latest advances in multi-agent differential games and presents applications in game theory and robotics. Framework for understanding a variety of methods and approaches in multi-agent machine learning. Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering"--
520			_a"Provide an in-depth coverage of multi-player, differential games and Gam theory"--
650		0	_aReinforcement learning.
650		0	_aDifferential games.
650		0	_aSwarm intelligence.
650		0	_aMachine learning.
650		7	_aTECHNOLOGY & ENGINEERING / Electronics / General. _2bisacsh
655		0	_aPrint books. _2local _94
942			_2lcc _cBOOKS
999			_c608494 _d608494