LEADER 05442nam 2200673Ia 450 001 9910139593803321 005 20170809165509.0 010 $a1-283-27370-5 010 $a9786613273703 010 $a1-118-02916-X 010 $a1-118-02917-8 010 $a1-118-02915-1 035 $a(CKB)2550000000054366 035 $a(EBL)697550 035 $a(OCoLC)757486955 035 $a(SSID)ssj0000550637 035 $a(PQKBManifestationID)11337043 035 $a(PQKBTitleCode)TC0000550637 035 $a(PQKBWorkID)10509693 035 $a(PQKB)11585926 035 $a(MiAaPQ)EBC697550 035 $a(PPN)170261689 035 $a(EXLCZ)992550000000054366 100 $a20101105d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApproximate dynamic programming$b[electronic resource] $esolving the curses of dimensionality /$fWarren B. Powell 205 $a2nd ed. 210 $aHoboken, N.J. $cJ. Wiley & Sons$dc2011 215 $a1 online resource (658 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-60445-X 320 $aIncludes bibliographical references and index. 327 $aApproximate Dynamic Programming; Contents; Preface to the Second Edition; Preface to the First Edition; Acknowledgments; 1 The Challenges of Dynamic Programming; 1.1 A Dynamic Programming Example: A Shortest Path Problem; 1.2 The Three Curses of Dimensionality; 1.3 Some Real Applications; 1.4 Problem Classes; 1.5 The Many Dialects of Dynamic Programming; 1.6 What Is New in This Book?; 1.7 Pedagogy; 1.8 Bibliographic Notes; 2 Some Illustrative Models; 2.1 Deterministic Problems; 2.2 Stochastic Problems; 2.3 Information Acquisition Problems; 2.4 A Simple Modeling Framework for Dynamic Programs 327 $a2.5 Bibliographic NotesProblems; 3 Introduction to Markov Decision Processes; 3.1 The Optimality Equations; 3.2 Finite Horizon Problems; 3.3 Infinite Horizon Problems; 3.4 Value Iteration; 3.5 Policy Iteration; 3.6 Hybrid Value-Policy Iteration; 3.7 Average Reward Dynamic Programming; 3.8 The Linear Programming Method for Dynamic Programs; 3.9 Monotone Policies*; 3.10 Why Does It Work?**; 3.11 Bibliographic Notes; Problems; 4 Introduction to Approximate Dynamic Programming; 4.1 The Three Curses of Dimensionality (Revisited); 4.2 The Basic Idea; 4.3 Q-Learning and SARSA 327 $a4.4 Real-Time Dynamic Programming4.5 Approximate Value Iteration; 4.6 The Post-Decision State Variable; 4.7 Low-Dimensional Representations of Value Functions; 4.8 So Just What Is Approximate Dynamic Programming?; 4.9 Experimental Issues; 4.10 But Does It Work?; 4.11 Bibliographic Notes; Problems; 5 Modeling Dynamic Programs; 5.1 Notational Style; 5.2 Modeling Time; 5.3 Modeling Resources; 5.4 The States of Our System; 5.5 Modeling Decisions; 5.6 The Exogenous Information Process; 5.7 The Transition Function; 5.8 The Objective Function; 5.9 A Measure-Theoretic View of Information** 327 $a5.10 Bibliographic NotesProblems; 6 Policies; 6.1 Myopic Policies; 6.2 Lookahead Policies; 6.3 Policy Function Approximations; 6.4 Value Function Approximations; 6.5 Hybrid Strategies; 6.6 Randomized Policies; 6.7 How to Choose a Policy?; 6.8 Bibliographic Notes; Problems; 7 Policy Search; 7.1 Background; 7.2 Gradient Search; 7.3 Direct Policy Search for Finite Alternatives; 7.4 The Knowledge Gradient Algorithm for Discrete Alternatives; 7.5 Simulation Optimization; 7.6 Why Does It Work?**; 7.7 Bibliographic Notes; Problems; 8 Approximating Value Functions; 8.1 Lookup Tables and Aggregation 327 $a8.2 Parametric Models8.3 Regression Variations; 8.4 Nonparametric Models; 8.5 Approximations and the Curse of Dimensionality; 8.6 Why Does It Work?**; 8.7 Bibliographic Notes; Problems; 9 Learning Value Function Approximations; 9.1 Sampling the Value of a Policy; 9.2 Stochastic Approximation Methods; 9.3 Recursive Least Squares for Linear Models; 9.4 Temporal Difference Learning with a Linear Model; 9.5 Bellman's Equation Using a Linear Model; 9.6 Analysis of TD(0), LSTD, and LSPE Using a Single State; 9.7 Gradient-Based Methods for Approximate Value Iteration* 327 $a9.8 Least Squares Temporal Differencing with Kernel Regression* 330 $aPraise for the First Edition ""Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners.""-Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems i 410 0$aWiley series in probability and statistics. 606 $aDynamic programming 606 $aProgramming (Mathematics) 615 0$aDynamic programming. 615 0$aProgramming (Mathematics) 676 $a519.7/03 676 $a519.703 700 $aPowell$b Warren B.$f1955-$0882830 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139593803321 996 $aApproximate dynamic programming$91972218 997 $aUNINA