Partially Observed Markov Decision Processes de Vikram Krishnamurthy

Partially Observed Markov Decision Processes

Vikram Krishnamurthy

This survey of formulation, algorithms, and structural results in POMDPs focuses on underlying concepts and connections to real-world applications in controlled sensing, keeping technical machinery to a minimum. The new edition includes inverse reinforcement learning, non-parametric Bayesian inference, variational Bayes and conformal prediction.

Citește tot Restrânge

	Preț	Express
Hardback (2)	657^.95 lei 43-57 zile
Cambridge University Press – 20 mar 2016	657^.95 lei 43-57 zile
Cambridge University Press – 28 apr 2025	725^.87 lei 43-57 zile	+94^.09 lei 6-12 zile

Preț

Express

Hardback (2)

657^.95 lei 43-57 zile

Cambridge University Press – 20 mar 2016

657^.95 lei 43-57 zile

Cambridge University Press – 28 apr 2025

725^.87 lei 43-57 zile

+94^.09 lei 6-12 zile

Cuprins

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.