Algorithms for decision making:
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, MA ; London, UK
The MIT Press
[2022]
|
| Schlagwörter: | |
| Links: | https://algorithmsbook.com/files/dm.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033860523&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xxii, 678 Seiten Illustrationen, Diagramme (teilweise farbig) |
| ISBN: | 9780262047012 |
Internformat
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Datensatz im Suchindex
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Contents Preface xix Acknowledgments 1 Introduction i i.i Decision Making 1.2 Applications 1.3 Methods 1.4 History 1.5 Societal Impact 1.6 Overview PART I 2 xxi i 2 5 7 12 14 PROBABILISTIC REASONING Representation 19 2.1 Degrees of Belief and Probability 2.2 Probability Distributions 2.3 Joint Distributions 2.4 Conditional Distributions 2.5 Bayesian Networks 2.6 Conditional Independence 2.7 Summary 36 2.8 Exercises 38 20 24 29 32 35 19
viii 3 CONTENTS Inference 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 4 97 Bayesian Network Scoring 97 Directed Graph Search 99 Markov Equivalence Classes 103 Partially Directed Graph Search 104 Summary 106 Exercises 107 Simple Decisions 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 71 Maximum Likelihood Parameter Learning Bayesian Parameter Learning 75 Nonparametric Learning 82 Learning with Missing Data 82 Summary 89 Exercises 89 Structure Learning 5.1 5.2 5.3 5.4 5.5 5.6 6 Inference in Bayesian Networks 43 Inference in Naive Bayes Models 48 Sum-Product Variable Elimination 49 Belief Propagation 53 Computational Complexity 53 Direct Sampling 54 Likelihood Weighted Sampling 57 Gibbs Sampling 60 Inference in Gaussian Models 63 Summary 65 Exercises 66 Parameter Learning 4.1 4.2 4.3 4.4 4.5 4.6 5 43 111 Constraints on Rational Preferences Utility Functions 112 Utility Elicitation 114 Maximum Expected Utility Principle Decision Networks 116 Value of Information 119 Irrationality 122 Summary 125 Exercises 125 111 116 71
CONTENTS PART II 7 8 9 SEQUENTIAL PROBLEMS 133 Exact Solution Methods 133 7.1 Markov Decision Processes 7.2 Policy Evaluation 7.3 Value Function Policies 7.4 7.5 Policy Iteration Value Iteration 7.6 Asynchronous Value Iteration 7.7 Linear Program Formulation 7.8 Linear Systems with Quadratic Reward 7.9 Summary 150 7.10 Exercises 151 136 139 140 141 Approximate Value Functions 147 161 8.1 Parametric Representations 8.2 Nearest Neighbor 163 8.3 Kernel Smoothing 164 8.4 Linear Interpolation 8.5 Simplex Interpolation 8.6 Linear Regression 8.7 Neural Network Regression 8.8 Summary 8.9 Exercises Online Planning 145 161 167 168 172 174 175 177 181 9.1 Receding Horizon Planning 9.2 Lookahead with Rollouts 9.3 Forward Search 9.4 Branch and Bound 9.5 Sparse Sampling 9.6 Monte Carlo Tree Search 9.7 Heuristic Search 9.8 Labeled Heuristic Search 183 185 187 9.9 Open-Loop Planning Summary 9.11 Exercises 209 187 197 9.10 208 181 183 197 200 147 IX
X CONTENTS io Policy Search 213 10.1 Approximate Policy Evaluation 10.2 Local Search ЮЗ IO.4 Genetic Algorithms Cross Entropy Method IO.5 Evolution Strategies 10.6 Isotropic Evolutionary Strategies 10.7 Summary 10.8 Exercises 215 218 219 226 231 11.1 Finite Difference 11.2 Regression Gradient 11-3 11.4 Likelihood Ratio 11-5 11.6 Baseline Subtraction 11-7 Exercises 231 234 234 Reward-to-Go 237 241 245 246 12 Policy Gradient Optimization 249 12.1 Gradient Ascent Update 12.2 Restricted Gradient Update 12.3 Natural Gradient Update 12.4 Trust Region Update 12.5 Clamped Surrogate Objective 12.6 Summary 12.7 Exercises 13 Actor-Critic Methods 224 226 11 Policy Gradient Estimation Summary 213 215 249 251 253 254 257 263 264 267 13.1 Actor-Critic 13.2 Generalized Advantage Estimation 133 Deterministic Policy Gradient 134 Actor-Critic with Monte Carlo Tree Search 13-5 13.6 Summary Exercises 267 277 277 269 272 274
CONTENTS 281 14 Policy Validation 14.1 Performance Metric Evaluation 14.2 Rare Event Simulation 14-3 Robustness Analysis 14-4 Trade Analysis 14-5 14.6 Adversarial Analysis 14-7 Exercises PART III Summary 281 285 288 289 291 295 295 MODEL UNCERTAINTY 15 Exploration and Exploitation 299 15.1 Bandit Problems 15.2 Bayesian Model Estimation 299 301 301 15.3 Undirected Exploration Strategies 15.4 Directed Exploration Strategies 303 15.5 Optimal Exploration Strategies 306 15.6 Exploration with Multiple States 15.7 Summary 15.8 Exercises 309 309 311 317 16 Model-Based Methods 16.1 Maximum Likelihood Models 16.2 Update Schemes 317 318 16.3 Exploration 16.4 Bayesian Methods 16.5 Bayes-Adaptive Markov Decision Processes 16.6 Posterior Sampling 16.7 Summary 332 16.8 Exercises 332 17 Model-Free Methods 321 326 330 335 17.1 Incremental Estimation of the Mean 17.2 Q-Learning 17.3 Sarsa 17.4 Eligibility Traces 336 338 341 335 329 Xi
xii CONTENTS Reward Shaping Vo 17.6 343 Action Value Function Approximation Experience Replay Summary 348 17.7 17.8 Exercises 17-9 18 Imitation Learning 343 345 351 355 18.1 18.2 Behavioral Cloning Data Set Aggregation 18.3 18.4 Stochastic Mixing Iterative Learning 358 Maximum Margin Inverse Reinforcement Learning 18.5 18.6 Maximum Entropy Inverse Reinforcement Learning 18.7 , Generative Adversarial Imitation Learning Summary 371 18.8 PART IV 19 Beliefs 19.1 19.2 19-3 19-4 19-5 19.6 355 358 Exercises 369 372 STATE UNCERTAINTY 379 Belief Initialization 379 Discrete State Filter 380 Kalman Filter 383 Extended Kalman Filter Unscented Kalman Filter 19-7 19.8 Particle Filter 390 Particle Injection 394 Summary 395 19-9 Exercises 385 387 397 20 Exact Belief State Planning 407 20.1 20.2 Belief-State Markov Decision Processes Conditional Plans 408 2Ο.3 20.4 Alpha Vectors Pruning 412 20.5 20.6 2Ο.7 Value Iteration 416 Linear Policies 419 Summary 419 20.8 Exercises 422 411 407 361 365
CONTENTS 21 Offline Belief State Planning 427 21.1 Fully Observable Value Approximation 21.2 Fast Informed Bound 21.3 Fast Lower Bounds 21.4 Point-Based Value Iteration 21.5 Randomized Point-Based Value Iteration 21.6 Sawtooth Upper Bound 21.7 Point Selection 21.8 Sawtooth Heuristic Search 21.9 Triangulated Value Functions 21.10 Summary 447 21.11 Exercises 448 429 430 431 436 440 22 Online Belief State Planning 22.1 442 445 453 Lookahead with Rollouts 453 22.2 Forward Search 22.3 Branch and Bound 22.4 Sparse Sampling 22.5 Monte Carlo Tree Search 22.6 Determinized Sparse Tree Search 22.7 Gap Heuristic Search 22.8 Summary 464 22.9 Exercises 467 453 23 Controller Abstractions 456 456 457 460 471 23.1 Controllers 23.2 Policy Iteration 23.3 Nonlinear Programming 23.4 Gradient Ascent 23.5 Summary 23.6 Exercises 471 486 486 427 475 481 478 459 433 xiii
XiV CONTENTS PART V MULTIAGENT SYSTEMS 493 24 Multiagent Reasoning 24.1 Simple Games 24.2 Response Models 24-3 Dominant Strategy Equilibrium 24-4 Nash Equilibrium 24-5 24.6 Correlated Equilibrium 24-7 24.8 Hierarchical Softmax 493 494 498 Gradient Ascent 24.11 Exercises Summary 504 505 24-9 24.10 509 509 511 517 25 Sequential Problems 25-1 25-2 Markov Games Response Models 519 25-3 Nash Equilibrium 520 254 Fictitious Play 25-5 25-6 Gradient Ascent 25-7 25.8 498 503 Iterated Best Response Fictitious Play Exercises 26 State Uncertainty 517 521 Nash Q-Learning Summary 497 526 526 528 530 533 26.1 Partially Observable Markov Games 26.2 Policy Evaluation 535 26.3 Nash Equilibrium 537 26.4 Dynamic Programming 26.5 Summary 26.6 Exercises 542 542 540 533
CONTENTS 27 Collaborative Agents 545 27.1 Decentralized Partially Observable Markov Decision Processes 27.2 Subclasses 27.3 Dynamic Programming 27.4 Iterated Best Response 27.5 Heuristic Search 27.6 Nonlinear Programming 27.7 Summary 27.8 Exercises 546 549 550 550 551 554 556 APPENDICES A 561 Mathematical Concepts 561 A.i Measure Spaces A.2 Probability Spaces A.3 Metric Spaces 562 562 A.4 Normed Vector Spaces A.5 Positive Definiteness A.6 Convexity A. 7 Information Content A.8 Entropy A.9 Cross Entropy A.10 Relative Entropy 567 A.11 Gradient Ascent 567 A. 12 Taylor Expansion A.13 Monte Carlo Estimation A.14 Importance Sampling 570 A.15 Contraction Mappings 570 A.16 Graphs 562 564 564 565 566 566 568 569 572 В Probability Distributions C Computational Complexity 573 575 575 C.i Asymptotic Notation C.2 Time Complexity Classes C.3 Space Complexity Classes C.4 Decidability 579 577 577 545
XVI D E CONTENTS Neural Representations 581 D.i Neural Networks D.2 Feedforward Networks D.3 Parameter Regularization D.4 Convolutional Neural Networks D.5 D.6 Recurrent Networks d.7 Adversarial Networks Search Algorithms E.2 582 585 594 599 Search Graphs 600 E.3 E.4 Forward Search E-5 E.6 Dynamic Programming Problems 592 599 Search Problems · 587 588 Autoencoder Networks E.i F 581 600 Branch and Bound 601 Heuristic Search 604 604 609 F.i Hex World F.2 2048 F-3 Cart-Pole f.4 Mountain Car F-5 F.6 Simple Regulator F-7 F.8 Crying Baby F.9 Catch F.10 Prisoner's Dilemma 621 609 610 611 612 613 Aircraft Collision Avoidance 614 615 Machine Replacement 617 619 F.11 Rock-Paper-Scissors 621 F.12 Traveler's Dilemma 622 E13 Predator-Prey Hex World F.i4 Multicaregiver Crying Baby E15 Collaborative Predator-Prey Hex World 623 624 625
CONTENTS G Julia 627 Types G.i G.2 627 Functions 640 Control Flow G.3 643 645 G.4 Packages G-5 Convenience Functions References Index 651 671 648 xvii |
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| spelling | Kochenderfer, Mykel J. 1980- Verfasser (DE-588)1077199945 aut Algorithms for decision making Mykel J. Kochenderfer, Tim Allan Wheeler, Kyle Hollins Wray Cambridge, MA ; London, UK The MIT Press [2022] © 2022 xxii, 678 Seiten Illustrationen, Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Computing: Textbooks & Study Guides COMPUTERS / Programming / Algorithms COMPUTERS / Data Science / Neural Networks Decision support systems - Mathematics Algorithms Entscheidungsfindung (DE-588)4113446-1 s Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s DE-604 Wheeler, Tim Allan Verfasser (DE-588)1194676790 aut Wray, Kyle Hollins Verfasser (DE-588)1270770659 aut Erscheint auch als Online-Ausgabe 978-0-262-37023-3 https://algorithmsbook.com/files/dm.pdf Verlag kostenfrei Volltext Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033860523&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kochenderfer, Mykel J. 1980- Wheeler, Tim Allan Wray, Kyle Hollins Algorithms for decision making Entscheidungsfindung (DE-588)4113446-1 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| subject_GND | (DE-588)4113446-1 (DE-588)4825546-4 |
| title | Algorithms for decision making |
| title_auth | Algorithms for decision making |
| title_exact_search | Algorithms for decision making |
| title_full | Algorithms for decision making Mykel J. Kochenderfer, Tim Allan Wheeler, Kyle Hollins Wray |
| title_fullStr | Algorithms for decision making Mykel J. Kochenderfer, Tim Allan Wheeler, Kyle Hollins Wray |
| title_full_unstemmed | Algorithms for decision making Mykel J. Kochenderfer, Tim Allan Wheeler, Kyle Hollins Wray |
| title_short | Algorithms for decision making |
| title_sort | algorithms for decision making |
| topic | Entscheidungsfindung (DE-588)4113446-1 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| topic_facet | Entscheidungsfindung Bestärkendes Lernen Künstliche Intelligenz |
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