ISR News Story
Marcus, Fu, Chang and Hu co-author Simulation-based Algorithms for Markov Decision Processes
A new book, Simulation-based Algorithms for Markov Decision Processes, has been co-written by four authors with ties to ISR. The new publication is in Springer's Communications and Control Engineering series. The authors are former ISR director and Professor Steven Marcus (ECE/ISR); Professor Michael Fu (Robert H. Smith School of Business/ISR); former ISR postdoctoral researcher Hyeong Soo Chang, now teaching at Sogang University in Seoul, Korea; and ISR alumnus Jiaqiao Hu, who was advised by Drs. Marcus and Fu and is now an assistant professor at SUNY Stonybrook.
Springer's summary of the book
• multi-stage adaptive sampling;
• evolutionary policy iteration;
• evolutionary random policy search; and
• model reference adaptive search.
Simulation-Based Algorithms for Markov Decision Processes brings this 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.
February 23, 2007