000			02395nam a22004218i 4500
005			20250919002920.0
008			150410s2014 nju bi 001 0 eng
020			_a9781118362082 _qhardback _cRM411.21
039		9	_a201507021558 _blan _c201507020958 _dlan _c201506181156 _drahah _y04-10-2015 _zrahah
040			_aDLC _beng _cDLC _erda _dUKM
090			_aQ325.6.S277 3
090			_aQ325.6 _b.S277 3
100	1		_aSchwartz, Howard M., _eeditor.
245	1	0	_aMulti-agent machine learning : _ba reinforcement approach / _cHoward M. Schwartz.
246	1	8	_ispine title : _aMulti-agent machine learning.
264		1	_aHoboken, NJ : _bJohn Wiley & Sons Inc., _c[2014].
264		4	_c©2014.
300			_axi, 242 pages : _billustrations ; _c24 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
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'-- _cProvided by publisher.
520			_a'Provide an in-depth coverage of multi-player, differential games and Gam theory'-- _cProvided by publisher.
650		0	_aReinforcement learning.
650		0	_aDifferential games.
650		0	_aSwarm intelligence.
650		0	_aMachine learning.
856	4	2	_3Cover image _uhttp://catalogimages.wiley.com/images/db/jimages/9781118362082.jpg.
907			_a.b16117852 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQ325.6.S277 3
914			_avtls003583484
990			_arab
991			_aFakulti Kejuruteraan dan Seni Bina
998			_al _b2015-10-04 _cm _da _feng _gnju _y0 _z.b16117852
999			_c590746 _d590746