Verfügbarkeit: Approximate dynamic programming

Approximate dynamic programming: solving the curses of dimensionality

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Bibliographische Detailangaben
1. Verfasser:	Powell, Warren B. 1955- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Hoboken, NJ J. Wiley & Sons 2007
Schriftenreihe:	Wiley series in probability and statistics
Schlagworte:	Programação dinâmica Dynamic programming Dynamische Optimierung
Online-Zugang:	Table of contents only Publisher description Contributor biographical information Inhaltsverzeichnis
Beschreibung:	Includes bibliographical references
Beschreibung:	XVI, 460 S. Ill., graph. Darst.
ISBN:	9780470171554

Internformat

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adam_text	APPROXIMATE DYNAMIC PROGRAMMING SOLVING THE CURSES OF DIMENSIONALITY WARREN B. POWELL PRINCETON UNIVERSITY PRINCETON, NEW JERSEY BICENTENNIAL B1CENTENNIAL WILEY-INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION CONTENTS PREFACE XI ACKNOWLEDGMENTS XV 1 THE CHALLENGES OF DYNAMIC PROGRAMMING 1 1.1 A DYNAMIC PROGRAMMING EXAMPLE: A SHORTEST PATH PROBLEM 2 1.2 THE THREE CURSES OF DIMENSIONALITY 3 1.3 SOME REAL APPLICATIONS 6 1.4 PROBLEM CLASSES 10 1.5 THE MANY DIALECTS OF DYNAMIC PROGRAMMING 12 1.6 WHAT IS NEW IN THIS BOOK? 14 1.7 BIBLIOGRAPHIE NOTES 16 2 SOME ILLUSTRATIVE MODEIS 17 2.1 DETERMINISTIC PROBLEMS 18 2.2 STOCHASTIC PROBLEMS 23 2.3 INFORMATION ACQUISITION PROBLEMS 36 2.4 A SIMPLE MODELING FRAMEWORK FOR DYNAMIC PROGRAMS 40 2.5 BIBLIOGRAPHIE NOTES 43 PROBLEMS 43 V VI CONTENTS INTRODUCTION TO MARKOV DECISION PROCESSES 47 3.1 THE OPTIMALITY EQUATIONS 48 3.2 FINITE HORIZON PROBLEMS 53 3.3 INFINITE HORIZON PROBLEMS 55 3.4 VALUE ITERATION 57 3.5 POLICY ITERATION 61 3.6 HYBRID VALUE-POLICY ITERATION 63 3.7 THE LINEAR PROGRAMMING METHOD FOR DYNAMIC PROGRAMS 63 3.8 MONOTONE POLICIES* 64 3.9 WHY DOES IT WORK? 70 3.10 BIBLIOGRAPHIE NOTES 85 PROBLEMS 86 INTRODUCTION TO APPROXIMATE DYNAMIC PROGRAMMING 91 4.1 THE THREE CURSES OF DIMENSIONALITY (REVISITED) 92 4.2 THE BASIC IDEA 93 4.3 SAMPLING RANDOM VARIABLES 100 4.4 ADP USING THE POST-DECISION STATE VARIABLE 101 4.5 LOW-DIMENSIONAL REPRESENTATIONS OF VALUE FUNETIONS 107 4.6 SO JUST WHAT IS APPROXIMATE DYNAMIC PROGRAMMING? 110 4.7 EXPERIMENTAL ISSUES 112 4.8 DYNAMIC PROGRAMMING WITH MISSING OR INCOMPLETE MODEIS 118 4.9 RELATIONSHIP TO REINFORCEMENT LEARNING 119 4.10 BUT DOES IT WORK? 120 4.11 BIBLIOGRAPHIE NOTES 122 PROBLEMS 123 MODELING DYNAMIC PROGRAMS 129 5.1 NOTATIONAL STYLE 131 5.2 MODELING TIME 132 5.3 MODELING RESOURCES 135 5.4 THE STATES OF OUR SYSTEM 139 5.5 MODELING DECISIONS 147 5.6 THE EXOGENOUS INFORMATION PROCESS 151 5.7 THE TRANSITION FUNETION 159 5.8 THE CONTRIBUTION FUNETION 166 5.9 THE OBJEETIVE FUNETION 169 5.10 A MEASURE-THEORETIC VIEW OF INFORMATION 170 5.11 BIBLIOGRAPHIE NOTES 173 PROBLEMS 173 CONTENTS VII 6 STOCHASTIC APPROXIMATION METHODS 179 6.1 A STOCHASTIC GRADIENT ALGORITHM 181 6.2 DETERMINISTIC STEPSIZE RECIPES 183 6.3 STOCHASTIC STEPSIZES , 190 6.4 COMPUTING BIAS AND VARIANCE 195 6.5 OPTIMAL STEPSIZES 197 6.6 SOME EXPERIMENTAL COMPARISONS OF STEPSIZE FORMULAS 204 6.7 CONVERGENCE 208 6.8 WHY DOES IT WORK? 210 6.9 BIBLIOGRAPHIE NOTES 220 PROBLEMS 221 7 APPROXIMATING VALUE FUNETIONS 225 7.1 APPROXIMATION USING AGGREGATION 226 7.2 APPROXIMATION METHODS USING REGRESSION MODEIS 237 7.3 RECURSIVE METHODS FOR REGRESSION MODEIS 246 7.4 NEURAL NETWORKS 253 7.5 VALUE FUNCTION APPROXIMATION FOR BATCH PROCESSES 257 7.6 WHY DOES IT WORK? 263 7.7 BIBLIOGRAPHIE NOTES 265 PROBLEMS 267 8 ADP FOR FINITE HORIZON PROBLEMS 271 8.1 STRATEGIES FOR FINITE HORIZON PROBLEMS 272 8.2 Q-LEARNING 276 8.3 TEMPORAL DIFFERENCE LEARNING 279 8.4 POLICY ITERATION 282 8.5 MONTE CARLO VALUE AND POLICY ITERATION 284 8.6 THE ACTOR-CRITIC PARADIGM 285 8.7 BIAS IN VALUE FUNCTION ESTIMATION 286 8.8 STATE SAMPLING STRATEGIES 290 8.9 STARTING AND STOPPING 294 8.10 A TAXONOMY OF APPROXIMATE DYNAMIC PROGRAMMING STRATEGIES 296 8.11 WHY DOES IT WORK 298 8.12 BIBLIOGRAPHIE NOTES 298 PROBLEMS 299 9 INFINITE HORIZON PROBLEMS 303 9.1 FROM FINITE TO INFINITE HORIZON 304 9.2 ALGORITHMIC STRATEGIES 304 9.3 STEPSIZES FOR INFINITE HORIZON PROBLEMS 313 VIII CONTENTS 9.4 ERROR MEASURES 315 9.5 DIRECT ADP FOR ON-LINE APPLICATIONS 317 9.6 FINITE HORIZON MODEIS FOR STEADY-STATE APPLICATIONS 317 9.7 WHY DOES IT WORK? 319 9.8 BIBLIOGRAPHIE NOTES 319 PROBLEMS 320 10 EXPLORATION VS. EXPLOITATION 323 10.1 A LEARNING EXERCISE: THE NOMADIC TRUCKER 323 10.2 LEARNING STRATEGIES 326 10.3 A SIMPLE INFORMATION ACQUISITION PROBLEM 330 10.4 GITTINS INDICES AND THE INFORMATION ACQUISITION PROBLEM 332 10.5 VARIATIONS 337 10.6 THE KNOWLEDGE GRADIENT ALGORITHM 339 10.7 INFORMATION ACQUISITION IN DYNAMIC PROGRAMMING 342 10.8 BIBLIOGRAPHIE NOTES 346 PROBLEMS 346 11 VALUE FUNCTION APPROXIMATIONS FOR SPECIAL FUNCTIONS 351 11.1 VALUE FUNETIONS VERSUS GRADIENTS 352 11.2 LINEAR APPROXIMATIONS 353 11.3 PIECEWISE LINEAR APPROXIMATIONS 355 11.4 THE SHAPE ALGORITHM 359 11.5 REGRESSION METHODS 362 11.6 CUTTING PLANES* 365 11.7 WHY DOES IT WORK?** 377 11.8 BIBLIOGRAPHIE NOTES 383 PROBLEMS 384 12 DYNAMIC RESOURCE ALLOCATION PROBLEMS 387 12.1 AN ASSET ACQUISITION PROBLEM 388 12.2 THE BLOOD MANAGEMENT PROBLEM 392 12.3 A PORTFOLIO OPTIMIZATION PROBLEM 401 12.4 A GENERAL RESOURCE ALLOCATION PROBLEM 404 12.5 A FLEET MANAGEMENT PROBLEM 416 12.6 A DRIVER MANAGEMENT PROBLEM 421 12.7 BIBLIOGRAPHIE REFERENCES 427 PROBLEMS 427 13 IMPLEMENTATION CHALLENGES 433 13.1 WILL ADP WORK FOR YOUR PROBLEM? 433 CONTENTS IX 13.2 DESIGNING AN ADP ALGORITHM FOR COMPLEX PROBLEMS 434 13.3 DEBUGGING AN ADP ALGORITHM 436 13.4 CONVERGENCE ISSUES 437 13.5 MODELING YOUR PROBLEM 438 13.6 ON-LINE VS. OFF-LINE MODEIS 440 13.7 IF IT WORKS, PATENT IT! 441 INDEX , 457
adam_txt	APPROXIMATE DYNAMIC PROGRAMMING SOLVING THE CURSES OF DIMENSIONALITY WARREN B. POWELL PRINCETON UNIVERSITY PRINCETON, NEW JERSEY BICENTENNIAL B1CENTENNIAL WILEY-INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION CONTENTS PREFACE XI ACKNOWLEDGMENTS XV 1 THE CHALLENGES OF DYNAMIC PROGRAMMING 1 1.1 A DYNAMIC PROGRAMMING EXAMPLE: A SHORTEST PATH PROBLEM 2 1.2 THE THREE CURSES OF DIMENSIONALITY 3 1.3 SOME REAL APPLICATIONS 6 1.4 PROBLEM CLASSES 10 1.5 THE MANY DIALECTS OF DYNAMIC PROGRAMMING 12 1.6 WHAT IS NEW IN THIS BOOK? 14 1.7 BIBLIOGRAPHIE NOTES 16 2 SOME ILLUSTRATIVE MODEIS 17 2.1 DETERMINISTIC PROBLEMS 18 2.2 STOCHASTIC PROBLEMS 23 2.3 INFORMATION ACQUISITION PROBLEMS 36 2.4 A SIMPLE MODELING FRAMEWORK FOR DYNAMIC PROGRAMS 40 2.5 BIBLIOGRAPHIE NOTES 43 PROBLEMS 43 V VI CONTENTS INTRODUCTION TO MARKOV DECISION PROCESSES 47 3.1 THE OPTIMALITY EQUATIONS 48 3.2 FINITE HORIZON PROBLEMS 53 3.3 INFINITE HORIZON PROBLEMS 55 3.4 VALUE ITERATION 57 3.5 POLICY ITERATION 61 3.6 HYBRID VALUE-POLICY ITERATION 63 3.7 THE LINEAR PROGRAMMING METHOD FOR DYNAMIC PROGRAMS 63 3.8 MONOTONE POLICIES* 64 3.9 WHY DOES IT WORK? 70 3.10 BIBLIOGRAPHIE NOTES 85 PROBLEMS 86 INTRODUCTION TO APPROXIMATE DYNAMIC PROGRAMMING 91 4.1 THE THREE CURSES OF DIMENSIONALITY (REVISITED) 92 4.2 THE BASIC IDEA 93 4.3 SAMPLING RANDOM VARIABLES 100 4.4 ADP USING THE POST-DECISION STATE VARIABLE 101 4.5 LOW-DIMENSIONAL REPRESENTATIONS OF VALUE FUNETIONS 107 4.6 SO JUST WHAT IS APPROXIMATE DYNAMIC PROGRAMMING? 110 4.7 EXPERIMENTAL ISSUES 112 4.8 DYNAMIC PROGRAMMING WITH MISSING OR INCOMPLETE MODEIS 118 4.9 RELATIONSHIP TO REINFORCEMENT LEARNING 119 4.10 BUT DOES IT WORK? 120 4.11 BIBLIOGRAPHIE NOTES 122 PROBLEMS 123 MODELING DYNAMIC PROGRAMS 129 5.1 NOTATIONAL STYLE 131 5.2 MODELING TIME 132 5.3 MODELING RESOURCES 135 5.4 THE STATES OF OUR SYSTEM 139 5.5 MODELING DECISIONS 147 5.6 THE EXOGENOUS INFORMATION PROCESS 151 5.7 THE TRANSITION FUNETION 159 5.8 THE CONTRIBUTION FUNETION 166 5.9 THE OBJEETIVE FUNETION 169 5.10 A MEASURE-THEORETIC VIEW OF INFORMATION 170 5.11 BIBLIOGRAPHIE NOTES 173 PROBLEMS 173 CONTENTS VII 6 STOCHASTIC APPROXIMATION METHODS 179 6.1 A STOCHASTIC GRADIENT ALGORITHM 181 6.2 DETERMINISTIC STEPSIZE RECIPES 183 6.3 STOCHASTIC STEPSIZES , 190 6.4 COMPUTING BIAS AND VARIANCE 195 6.5 OPTIMAL STEPSIZES 197 6.6 SOME EXPERIMENTAL COMPARISONS OF STEPSIZE FORMULAS 204 6.7 CONVERGENCE ' 208 6.8 WHY DOES IT WORK? 210 6.9 BIBLIOGRAPHIE NOTES 220 PROBLEMS 221 7 APPROXIMATING VALUE FUNETIONS 225 7.1 APPROXIMATION USING AGGREGATION 226 7.2 APPROXIMATION METHODS USING REGRESSION MODEIS 237 7.3 RECURSIVE METHODS FOR REGRESSION MODEIS 246 7.4 NEURAL NETWORKS 253 7.5 VALUE FUNCTION APPROXIMATION FOR BATCH PROCESSES 257 7.6 WHY DOES IT WORK? 263 7.7 BIBLIOGRAPHIE NOTES 265 PROBLEMS 267 8 ADP FOR FINITE HORIZON PROBLEMS 271 8.1 STRATEGIES FOR FINITE HORIZON PROBLEMS 272 8.2 Q-LEARNING 276 8.3 TEMPORAL DIFFERENCE LEARNING 279 8.4 POLICY ITERATION 282 8.5 MONTE CARLO VALUE AND POLICY ITERATION 284 8.6 THE ACTOR-CRITIC PARADIGM 285 8.7 BIAS IN VALUE FUNCTION ESTIMATION 286 8.8 STATE SAMPLING STRATEGIES 290 8.9 STARTING AND STOPPING 294 8.10 A TAXONOMY OF APPROXIMATE DYNAMIC PROGRAMMING STRATEGIES 296 8.11 WHY DOES IT WORK 298 8.12 BIBLIOGRAPHIE NOTES 298 PROBLEMS 299 9 INFINITE HORIZON PROBLEMS 303 9.1 FROM FINITE TO INFINITE HORIZON 304 9.2 ALGORITHMIC STRATEGIES 304 9.3 STEPSIZES FOR INFINITE HORIZON PROBLEMS 313 VIII CONTENTS 9.4 ERROR MEASURES 315 9.5 DIRECT ADP FOR ON-LINE APPLICATIONS 317 9.6 FINITE HORIZON MODEIS FOR STEADY-STATE APPLICATIONS 317 9.7 WHY DOES IT WORK? 319 9.8 BIBLIOGRAPHIE NOTES 319 PROBLEMS 320 10 EXPLORATION VS. EXPLOITATION 323 10.1 A LEARNING EXERCISE: THE NOMADIC TRUCKER 323 10.2 LEARNING STRATEGIES 326 10.3 A SIMPLE INFORMATION ACQUISITION PROBLEM 330 10.4 GITTINS INDICES AND THE INFORMATION ACQUISITION PROBLEM 332 10.5 VARIATIONS 337 10.6 THE KNOWLEDGE GRADIENT ALGORITHM 339 10.7 INFORMATION ACQUISITION IN DYNAMIC PROGRAMMING 342 10.8 BIBLIOGRAPHIE NOTES 346 PROBLEMS 346 11 VALUE FUNCTION APPROXIMATIONS FOR SPECIAL FUNCTIONS 351 11.1 VALUE FUNETIONS VERSUS GRADIENTS 352 11.2 LINEAR APPROXIMATIONS 353 11.3 PIECEWISE LINEAR APPROXIMATIONS 355 11.4 THE SHAPE ALGORITHM 359 11.5 REGRESSION METHODS 362 11.6 CUTTING PLANES* 365 11.7 WHY DOES IT WORK?** 377 11.8 BIBLIOGRAPHIE NOTES 383 PROBLEMS 384 12 DYNAMIC RESOURCE ALLOCATION PROBLEMS 387 12.1 AN ASSET ACQUISITION PROBLEM 388 12.2 THE BLOOD MANAGEMENT PROBLEM 392 12.3 A PORTFOLIO OPTIMIZATION PROBLEM 401 12.4 A GENERAL RESOURCE ALLOCATION PROBLEM 404 12.5 A FLEET MANAGEMENT PROBLEM 416 12.6 A DRIVER MANAGEMENT PROBLEM 421 12.7 BIBLIOGRAPHIE REFERENCES 427 PROBLEMS 427 13 IMPLEMENTATION CHALLENGES 433 13.1 WILL ADP WORK FOR YOUR PROBLEM? 433 CONTENTS IX 13.2 DESIGNING AN ADP ALGORITHM FOR COMPLEX PROBLEMS 434 13.3 DEBUGGING AN ADP ALGORITHM 436 13.4 CONVERGENCE ISSUES 437 13.5 MODELING YOUR PROBLEM 438 13.6 ON-LINE VS. OFF-LINE MODEIS 440 13.7 IF IT WORKS, PATENT IT! 441 INDEX , 457
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spelling	Powell, Warren B. 1955- Verfasser (DE-588)170298175 aut Approximate dynamic programming solving the curses of dimensionality Warren B. Powell Hoboken, NJ J. Wiley & Sons 2007 XVI, 460 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Includes bibliographical references Programação dinâmica larpcal Dynamic programming Dynamische Optimierung (DE-588)4125677-3 gnd rswk-swf Dynamische Optimierung (DE-588)4125677-3 s DE-604 http://www.loc.gov/catdir/toc/ecip0714/2007013724.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0739/2007013724-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0742/2007013724-b.html Contributor biographical information GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016159183&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Powell, Warren B. 1955- Approximate dynamic programming solving the curses of dimensionality Programação dinâmica larpcal Dynamic programming Dynamische Optimierung (DE-588)4125677-3 gnd
subject_GND	(DE-588)4125677-3
title	Approximate dynamic programming solving the curses of dimensionality
title_auth	Approximate dynamic programming solving the curses of dimensionality
title_exact_search	Approximate dynamic programming solving the curses of dimensionality
title_exact_search_txtP	Approximate dynamic programming solving the curses of dimensionality
title_full	Approximate dynamic programming solving the curses of dimensionality Warren B. Powell
title_fullStr	Approximate dynamic programming solving the curses of dimensionality Warren B. Powell
title_full_unstemmed	Approximate dynamic programming solving the curses of dimensionality Warren B. Powell
title_short	Approximate dynamic programming
title_sort	approximate dynamic programming solving the curses of dimensionality
title_sub	solving the curses of dimensionality
topic	Programação dinâmica larpcal Dynamic programming Dynamische Optimierung (DE-588)4125677-3 gnd
topic_facet	Programação dinâmica Dynamic programming Dynamische Optimierung
url	http://www.loc.gov/catdir/toc/ecip0714/2007013724.html http://www.loc.gov/catdir/enhancements/fy0739/2007013724-d.html http://www.loc.gov/catdir/enhancements/fy0742/2007013724-b.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016159183&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT powellwarrenb approximatedynamicprogrammingsolvingthecursesofdimensionality

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