Partially Observed Markov Decision Processes

Name: Partially Observed Markov Decision Processes
Author: Vikram Krishnamurthy

From Filtering to Controlled Sensing

Gebonden Engels 2016 9781107134607

120,89

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Samenvatting

Covering formulation, algorithms, and structural results, and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. Bringing together research from across the literature, the book provides an introduction to nonlinear filtering followed by a systematic development of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP have a threshold optimal policy? When are myopic policies optimal? How do local and global decision makers interact in adaptive decision making in multi-agent social learning where there is herding and data incest? And how can sophisticated radars and sensors adapt their sensing in real time?

Specificaties

ISBN13:9781107134607

Taal:Engels

Bindwijze:gebonden

Aantal pagina's:488

Uitgever:Cambridge University Press

Verschijningsdatum:21-3-2016

Hoofdrubriek:Wetenschap en techniek

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Inhoudsopgave

Preface; 1. Introduction; Part I. Stochastic Models and Bayesian Filtering: 2. Stochastic state-space models; 3. Optimal filtering; 4. Algorithms for maximum likelihood parameter estimation; 5. Multi-agent sensing: social learning and data incest; Part II. Partially Observed Markov Decision Processes. Models and Algorithms: 6. Fully observed Markov decision processes; 7. Partially observed Markov decision processes (POMDPs); 8. POMDPs in controlled sensing and sensor scheduling; Part III. Partially Observed Markov Decision Processes: 9. Structural results for Markov decision processes; 10. Structural results for optimal filters; 11. Monotonicity of value function for POMPDs; 12. Structural results for stopping time POMPDs; 13. Stopping time POMPDs for quickest change detection; 14. Myopic policy bounds for POMPDs and sensitivity to model parameters; Part IV. Stochastic Approximation and Reinforcement Learning: 15. Stochastic optimization and gradient estimation; 16. Reinforcement learning; 17. Stochastic approximation algorithms: examples; 18. Summary of algorithms for solving POMPDs; Appendix A. Short primer on stochastic simulation; Appendix B. Continuous-time HMM filters; Appendix C. Markov processes; Appendix D. Some limit theorems; Bibliography; Index.