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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Vikram Krishnamurthy is Professor of Electrical and Computer Engineering at Cornell University. From 2002 to 2016, he was Professor and Senior Canada Research Chair in Statistical Signal Processing at the University of British Columbia. His research contributions are in statistical signal processing, stochastic optimization and control, with applications in social networks, adaptive radar systems and biological ion channels. He is a Fellow of IEEE and served as Distinguished Lecturer for the IEEE Signal Processing Society and Editor-in-Chief of IEEE Journal of Selected Topics in Signal Processing. He was awarded an honorary doctorate from the Royal Institute of Technology (KTH) Sweden in 2014.
Inhaltsangabe
Preface to revised edition Notation 1. Introduction I. Stochastic Models and Bayesian Filtering: 2. Stochastic state space model 3. Optimal filtering 4. Algorithms for maximum likelihood parameter estimation 5. Multi-agent sensing: social learning and data incest 6. Nonparametric Bayesian inference II. POMDPs: Models and Applications: 7. Fully observed Markov decision processes 8. Partially observed Markov decision processes 9. POMDPs in controlled sensing and sensor scheduling III. POMDP Structural Results: 10. Structural results for Markov decision processes 11. Structural results for optimal filters 12. Monotonicity of value function for POMDPs 13. Structural results for stopping-time POMDPs 14. Stopping-Time POMDPs for quickest detection 15. Myopic policy bounds for POMDPs and sensitivity to model parameters IV. Stochastic Gradient Algorithms and Reinforcement Learning: 16. Stochastic optimization and gradient estimation 17. Reinforcement learning 18. Stochastic gradient algorithms: convergence analysis 19. Discrete stochastic optimization V. Inverse Reinforcement Learning: 20. Revealed preferences for inverse reinforcement learning 21. Bayesian inverse reinforcement learning Appendix A. Short primer on stochastic stimulation Appendix B. Continuous-time HMM filters Appendix C. Discrete-time Martingales Appendix D. Markov processes Appendix E. Some limit theorems in statistics Appendix F. Summary of POMDP algorithms Bibliography Index.
Preface to revised edition Notation 1. Introduction I. Stochastic Models and Bayesian Filtering: 2. Stochastic state space model 3. Optimal filtering 4. Algorithms for maximum likelihood parameter estimation 5. Multi-agent sensing: social learning and data incest 6. Nonparametric Bayesian inference II. POMDPs: Models and Applications: 7. Fully observed Markov decision processes 8. Partially observed Markov decision processes 9. POMDPs in controlled sensing and sensor scheduling III. POMDP Structural Results: 10. Structural results for Markov decision processes 11. Structural results for optimal filters 12. Monotonicity of value function for POMDPs 13. Structural results for stopping-time POMDPs 14. Stopping-Time POMDPs for quickest detection 15. Myopic policy bounds for POMDPs and sensitivity to model parameters IV. Stochastic Gradient Algorithms and Reinforcement Learning: 16. Stochastic optimization and gradient estimation 17. Reinforcement learning 18. Stochastic gradient algorithms: convergence analysis 19. Discrete stochastic optimization V. Inverse Reinforcement Learning: 20. Revealed preferences for inverse reinforcement learning 21. Bayesian inverse reinforcement learning Appendix A. Short primer on stochastic stimulation Appendix B. Continuous-time HMM filters Appendix C. Discrete-time Martingales Appendix D. Markov processes Appendix E. Some limit theorems in statistics Appendix F. Summary of POMDP algorithms Bibliography Index.
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