Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part I Stochastic Models and Bayesian Filtering
- Part II Partially Observed Markov Decision Processes: Models and Applications
- Part III Partially Observed Markov Decision Processes: Structural Results
- 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 POMDPs
- Appendix A Short primer on stochastic simulation
- Appendix B Continuous-time HMM filters
- Appendix C Markov processes
- Appendix D Some limit theorems
- References
- Index
15 - Stochastic optimization and gradient estimation
from Part IV - Stochastic Approximation and Reinforcement Learning
Published online by Cambridge University Press: 05 April 2016
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part I Stochastic Models and Bayesian Filtering
- Part II Partially Observed Markov Decision Processes: Models and Applications
- Part III Partially Observed Markov Decision Processes: Structural Results
- 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 POMDPs
- Appendix A Short primer on stochastic simulation
- Appendix B Continuous-time HMM filters
- Appendix C Markov processes
- Appendix D Some limit theorems
- References
- Index
Summary
- Type
- Chapter
- Information
- Partially Observed Markov Decision ProcessesFrom Filtering to Controlled Sensing, pp. 343 - 363Publisher: Cambridge University PressPrint publication year: 2016