Markov Decision Processes in Artificial Intelligence
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Markov Decision Processes in Artificial Intelligence

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Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications.
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Product details

  • Hardback | 477 pages
  • 160 x 239 x 31mm | 812g
  • London, United Kingdom
  • English
  • 1. Auflage
  • 1848211678
  • 9781848211674
  • 1,640,382

Table of contents

Preface xvii List of Authors xix PART 1. MDPS: MODELS AND METHODS 1 Chapter 1. Markov Decision Processes 3 Frederick GARCIA and Emmanuel RACHELSON 1.1. Introduction 3 1.2. Markov decision problems 4 1.3. Value functions 9 1.4. Markov policies 12 1.5. Characterization of optimal policies 14 1.6. Optimization algorithms for MDPs 28 1.7. Conclusion and outlook 37 1.8. Bibliography 37 Chapter 2. Reinforcement Learning 39 Olivier SIGAUD and Frederick GARCIA 2.1. Introduction 39 2.2. Reinforcement learning: a global view 40 2.3. Monte Carlo methods 45 2.4. From Monte Carlo to temporal difference methods 45 2.5. Temporal difference methods 46 2.6. Model-based methods: learning a model 59 2.7. Conclusion 63 2.8. Bibliography 63 Chapter 3. Approximate Dynamic Programming 67 Remi MUNOS 3.1. Introduction 68 3.2. Approximate value iteration (AVI) 70 3.3. Approximate policy iteration (API) 77 3.4. Direct minimization of the Bellman residual 87 3.5. Towards an analysis of dynamic programming in Lp-norm 88 3.6. Conclusions 93 3.7. Bibliography 93 Chapter 4. Factored Markov Decision Processes 99 Thomas DEGRIS and Olivier SIGAUD 4.1. Introduction 99 4.2. Modeling a problem with an FMDP 100 4.3. Planning with FMDPs 108 4.4. Perspectives and conclusion 122 4.5. Bibliography 123 Chapter 5. Policy-Gradient Algorithms 127 Olivier BUFFET 5.1. Reminder about the notion of gradient 128 5.2. Optimizing a parameterized policy with a gradient algorithm 130 5.3. Actor-critic methods 143 5.4. Complements 147 5.5. Conclusion 150 5.6. Bibliography 150 Chapter 6. Online Resolution Techniques 153 Laurent PERET and Frederick GARCIA 6.1. Introduction 153 6.2. Online algorithms for solving an MDP 155 6.3. Controlling the search 167 6.4. Conclusion 180 6.5. Bibliography 180 PART 2. BEYOND MDPS 185 Chapter 7. Partially Observable Markov Decision Processes 187 Alain DUTECH and Bruno SCHERRER 7.1. Formal definitions for POMDPs 188 7.2. Non-Markovian problems: incomplete information 196 7.3. Computation of an exact policy on information states 202 7.4. Exact value iteration algorithms 207 7.5. Policy iteration algorithms 222 7.6. Conclusion and perspectives 223 7.7. Bibliography 225 Chapter 8. Stochastic Games 229 Andriy BURKOV, Laetitia MATIGNON and Brahim CHAIB-DRAA 8.1. Introduction 229 8.2. Background on game theory 230 8.3. Stochastic games 245 8.4. Conclusion and outlook 269 8.5. Bibliography 270 Chapter 9. DEC-MDP/POMDP 277 Aurelie BEYNIER, Francois CHARPILLET, Daniel SZER and Abdel-Illah MOUADDIB 9.1. Introduction 277 9.2. Preliminaries 278 9.3. Multi agent Markov decision processes 279 9.4. Decentralized control and local observability 280 9.5. Sub-classes of DEC-POMDPs 285 9.6. Algorithms for solving DEC-POMDPs 295 9.7. Applicative scenario: multirobot exploration 310 9.8. Conclusion and outlook ... 312 9.9. Bibliography 313 Chapter 10. Non-Standard Criteria 319 Matthieu BOUSSARD, Maroua BOUZID, Abdel-Illah MOUADDIB, Regis SABBADIN and Paul WENG 10.1. Introduction 319 10.2. Multicriteria approaches 320 10.3. Robustness in MDPs 327 10.4. Possibilistic MDPs 329 10.5. Algebraic MDPs 342 10.6. Conclusion 354 10.7. Bibliography 355 PART 3. APPLICATIONS 361 Chapter 11. Online Learning for Micro-Object Manipulation 363 Guillaume LAURENT 11.1. Introduction 363 11.2. Manipulation device 364 11.3. Choice of the reinforcement learning algorithm 367 11.4. Experimental results 370 11.5. Conclusion 373 11.6. Bibliography 373 Chapter 12. Conservation of Biodiversity 375 Iadine CHADES 12.1. Introduction 375 12.2. When to protect, survey or surrender cryptic endangered species 376 12.3. Can sea otters and abalone co-exist? 381 12.4. Other applications in conservation biology and discussions 391 12.5. Bibliography 392 Chapter 13. Autonomous Helicopter Searching for a Landing Area in an Uncertain Environment 395 Patrick FABIANI and Florent TEICHTEIL-KONIGSBUCH 13.1. Introduction 395 13.2. Exploration scenario 397 13.3. Embedded control and decision architecture 401 13.4. Incremental stochastic dynamic programming 404 13.5. Flight tests and return on experience 407 13.6. Conclusion 410 13.7. Bibliography 410 Chapter 14. Resource Consumption Control for an Autonomous Robot 413 Simon LE GLOANNEC and Abdel-Illah MOUADDIB 14.1. The rover s mission 414 14.2. Progressive processing formalism 415 14.3. MDP/PRU model 416 14.4. Policy calculation 418 14.5. How to model a real mission 419 14.6. Extensions 422 14.7. Conclusion 423 14.8. Bibliography 423 Chapter 15. Operations Planning 425 Sylvie THIEBAUX and Olivier BUFFET 15.1. Operations planning 425 15.2. MDP value function approaches 433 15.3. Reinforcement learning: FPG 442 15.4. Experiments 446 15.5. Conclusion and outlook 448 15.6. Bibliography 450 Index 453
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Review Text

"As an overall conclusion, this book is an extensive presentation of MDPs and their applications in modeling uncertain decision problems and in reinforcement learning." (Zentralblatt MATH, 2011)
"The range of subjects covered is fascinating, however, from game-theoretical applications to reinforcement learning, conservation of biodiversity and operations planning. Oriented towards advanced students and researchers in the fields of both artificial intelligence and the study of algorithms as well as discrete mathematics." ( Book News , September 2010)
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Review quote

"As an overall conclusion, this book is an extensive presentation of MDPs and their applications in modeling uncertain decision problems and in reinforcement learning." (Zentralblatt MATH, 2011) "The range of subjects covered is fascinating, however, from game-theoretical applications to reinforcement learning, conservation of biodiversity and operations planning. Oriented towards advanced students and researchers in the fields of both artificial intelligence and the study of algorithms as well as discrete mathematics." (Book News, September 2010)
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About Olivier Sigaud

Olivier Sigaud is a Professor of Computer Science at the University of Paris 6 (UPMC). He is the Head of the "Motion" Group in the Institute of Intelligent Systems and Robotics (ISIR).
Olivier Buffet has been an INRIA researcher in the Autonomous Intelligent Machines (MAIA) team of theLORIA laboratory, since November 2007.
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