000			04933cam a2200421Mi 4500
001			ocn830162389
003			OCoLC
005			20240726105342.0
008			130316s2012 si o 000 0 eng d
040			_aEBLCP _beng _epn _erda _cEBLCP _dOCLCO _dYDXCP _dNT _dDEBSZ _dOCLCQ _dOCLCF _dOCLCQ
020			_a9781848167940 _q((electronic)l(electronic)ctronic)
050	0	4	_aQA274 _b.E936 2012
049			_aMAIN
100	1		_aPiunovskiy, A. B. _e1
245	1	0	_aExamples in Markov Decision Processes
260			_aSingapore : _bWorld Scientific Publishing Company, _c(c)2012.
300			_a1 online resource (308 pages).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_adata file _2rda
490	1		_aImperial College Press Optimization Series ; _vv. 2
504			_a2
505	0	0	_aPreface; 1. Finite-Horizon Models; 1.1 Preliminaries; 1.2 Model Description; 1.3 Dynamic Programming Approach; 1.4 Examples; 1.4.1 Non-transitivity of the correlation; 1.4.2 The more frequently used control is not better; 1.4.3 Voting; 1.4.4 The secretary problem; 1.4.5 Constrained optimization; 1.4.6 Equivalent Markov selectors in non-atomic MDPs; 1.4.7 Strongly equivalent Markov selectors in nonatomic MDPs; 1.4.8 Stock exchange; 1.4.9 Markov or non-Markov strategy? Randomized or not? When is the Bellman principle violated?; 1.4.10 Uniformly optimal, but not optimal strategy.
505	0	0	_a1.4.11 Martingales and the Bellman principle1.4.12 Conventions on expectation and infinities; 1.4.13 Nowhere-differentiable function vt(x); discontinuous function vt(x); 1.4.14 The non-measurable Bellman function; 1.4.15 No one strategy is uniformly -optimal; 1.4.16 Semi-continuous model; 2. Homogeneous Infinite-Horizon Models: Expected Total Loss; 2.1 Homogeneous Non-discounted Model; 2.2 Examples; 2.2.1 Mixed Strategies; 2.2.2 Multiple solutions to the optimality equation; 2.2.3 Finite model: multiple solutions to the optimality equation; conserving but not equalizing strategy.
505	0	0	_a2.2.4 The single conserving strategy is not equalizing and not optimal2.2.5 When strategy iteration is not successful; 2.2.6 When value iteration is not successful; 2.2.7 When value iteration is not successful: positive model I; 2.2.8 When value iteration is not successful: positive model II; 2.2.9 Value iteration and stability in optimal stopping problems; 2.2.10 A non-equalizing strategy is uniformly optimal; 2.2.11 A stationary uniformly -optimal selector does not exist (positive model); 2.2.12 A stationary uniformly -optimal selector does not exist (negative model).
505	0	0	_a2.2.13 Finite-action negative model where a stationary uniformly -optimal selector does not exist2.2.14 Nearly uniformly optimal selectors in negative models; 2.2.15 Semi-continuous models and the blackmailer's dilemma; 2.2.16 Not a semi-continuous model; 2.2.17 The Bellman function is non-measurable and no one strategy is uniformly -optimal; 2.2.18 A randomized strategy is better than any selector (finite action space); 2.2.19 The fluid approximation does not work; 2.2.20 The fluid approximation: refined model; 2.2.21 Occupation measures: phantom solutions.
505	0	0	_a2.2.22 Occupation measures in transient models2.2.23 Occupation measures and duality; 2.2.24 Occupation measures: compactness; 2.2.25 The bold strategy in gambling is not optimal (house limit); 2.2.26 The bold strategy in gambling is not optimal (inflation); 2.2.27 Search strategy for a moving target; 2.2.28 The three-way duel ("Truel"); 3. Homogeneous Infinite-Horizon Models: Discounted Loss; 3.1 Preliminaries; 3.2 Examples; 3.2.1 Phantom solutions of the optimality equation; 3.2.2 When value iteration is not successful: positive model.
500			_a3.2.3 A non-optimal strategy for which v x solves the optimality equation.
520	0		_aThis invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions imposed in the theorems on Markov Decision Processes. Many of the examples are based upon examples published earlier in journal articles or textbooks while several other examples are new. The aim was.
530			_a2 _ub
650		0	_aMarkov processes.
655		1	_aElectronic Books.
856	4	0	_uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=545467&site=eds-live&custid=s3260518 _zClick to access digital title | log in using your CIU ID number and my.ciu.edu password
942			_cOB _D _eEB _hQA. _m2012 _QOL _R _x _8NFIC _2LOC
994			_a92 _bNT
999			_c97514 _d97514
902			_a1 _bCynthia Snell _c1 _dCynthia Snell