Approximate dynamic programming: solving the curses of dimensionality
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ
J. Wiley & Sons
2007
|
| Series: | Wiley series in probability and statistics
|
| Subjects: | |
| Online Access: | Table of contents only Publisher description Contributor biographical information Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references |
| Physical Description: | XVI, 460 S. Ill., graph. Darst. |
| ISBN: | 9780470171554 |
Staff View
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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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| discipline_str_mv | Informatik Mathematik Wirtschaftswissenschaften |
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| index_date | 2024-07-02T19:02:52Z |
| indexdate | 2024-07-09T21:08:30Z |
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| isbn | 9780470171554 |
| language | English |
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| record_format | marc |
| series2 | Wiley series in probability and statistics |
| 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 |