000			03249nam a2200385 i 4500
001			63456
008			180515s2018 sz a b 001 0 eng d
020			_a9783319790336 _q(electronic bk.)
020			_a3319790331 _q(electronic bk.)
020			_z9783319790329 _q(print)
020			_z3319790323 _q(paperback)
035			_a(OCoLC)1035635968 _z(OCoLC)1035845367 _z(OCoLC)1036267352 _z(OCoLC)1037031598
040			_aGW5XE _beng _cGW5XE _dN$T _dEBLCP _dYDX _dN$T _dUAB _dOCLCF _dOCLCQ _dFIE _dSTF _dBAUN _erda
041	0		_aeng
049			_aBAUN_MERKEZ
050	1	4	_aQA402.37 _b.S35 2018
082	0	4	_223
100	1		_aSaldi, Naci, _eaut _9118739
245	1	0	_aFinite approximations in discrete-time stochastic control : _bquantized models and asymptotic optimality / _cNaci Saldi, Tamás Linder, Serdar Yüksel
264		1	_aCham, Switzerland : _bBirkhäuser, _c[2018]
300			_avii, 198 pages : _billustrations ; _c23 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aSystems & control: foundations & applications, _x2324-9749
504			_aIncludes bibliographical references and index
505	0	0	_t--Introduction and Summary _t-- Part I: Finite Model Approximations in Stochastic Control _t-- Prelude to Part I _t-- Finite Action Approximation of Markov Decision Processes _t-- Finite-State Approximation of Markov Decision Processes _t-- Approximations for Partially Observed Markov Decision Processes _t-- Approximations for Constrained Markov Decision Problems _t-- Part II: Finite Model Approximations in Decentralized Stochastic Control _t-- Prelude to Part II _t-- Finite Model Approximations in Decentralized Stochastic Control _t-- Asymptotic Optimality of Finite Models for Specific Systems _t-- Index _t-- References
520			_aIn a unified form, this monograph presents fundamental results on the approximation of centralized and decentralized stochastic control problems, with uncountable state, measurement, and action spaces. It demonstrates how quantization provides a system-independent and constructive method for the reduction of a system with Borel spaces to one with finite state, measurement, and action spaces. In addition to this constructive view, the book considers both the information transmission approach for discretization of actions, and the computational approach for discretization of states and actions. Part I of the text discusses Markov decision processes and their finite-state or finite-action approximations, while Part II builds from there to finite approximations in decentralized stochastic control problems. This volume is perfect for researchers and graduate students interested in stochastic controls. With the tools presented, readers will be able to establish the convergence of approximation models to original models and the methods are general enough that researchers can build corresponding approximation results, typically with no additional assumptions.--
650		0	_aStochastic control theory. _9100474
650		0	_aApproximation theory. _9118740
700	1		_aLinder, Tamás, _eaut _9118742
700	1		_aYüksel, Serdar _eaut _9118741
830		0	_aSystems & control. _9110213
942			_2lcc _cKT
999			_c92055 _d92055