000			01676cam a2200265 i 4500
005			20160322165210.0
008			140604t20142014njua b 001 0 eng
020			_a9781118362082 (hardback)
020			_a111836208X (hardback)
041			_aeng
082	0	0	_a004.85 _bSCH
100	1		_aSchwartz, Howard M., _92400
245	1	0	_aMulti-agent machine learning : _ba reinforcement approach / _cHoward M. Schwartz, Department of Systems and Computer Engineering, Carleton University.
260			_aNew Jersey: _bWiley, _c2014.
300			_axi, 242 pages :
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. _92401
650		0	_aDifferential games. _92402
650		0	_aSwarm intelligence. _92403
650		0	_aMachine learning. _955
650		7	_aTECHNOLOGY & ENGINEERING / Electronics / General. _9230
856	4	2	_uhttp://catalogimages.wiley.com/images/db/jimages/9781118362082.jpg
942			_cBK
999			_c156020 _d156020