Simulation-based algorithms for Markov decision processes

"Simulation-based Algorithms for Markov Decision Processes brings, state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality." "The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling and control, and simulation but will be a valuable source of instruction and reference for students of control and operations research."--BOOK JACKET.

Year

2007

Language

English

Series

Communications and control engineering

Publisher

Springer

Editions

1

Pages

189

ISBN

9781846286896

Data source: Wikidata