作者：
Suresh KalyanasundaramEdwin K. P. ChongNess B. ShroffMotorola India Electronics Limited
No. 66/1 Plot 5 Bagmane Techpark C. V. Raman Nagar Post Bangalore 560 093 India. Department of Electrical and Computer Engineering
Colorado State University Fort Collins CO 80523-1373 USA. Professor Edwin K. P. Chong received the B.E.(Hons.) degree with First Class Honors from the University of Adelaide
South Australia in 1987 graduating top of his class and the M.A. and Ph.D. degrees in 1989 and 1991
respectively both from Princeton University where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering at Purdue University in 1991 where he was named a University Faculty Scholar in 1999 and promoted to Full Professor in 2001. Since August 2001 he has been a Professor of Electrical and Computer Engineering and Professor of Mathematics at Colorado State University. His current interests are in communication networks and optimization methods. He coauthored the best-selling book An Introduction to Optimization 2nd Edition Wiley-Interscience 2001. He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He coauthored a paper that was awarded Best Paper in the journal Computer Networks 2003. Professor Chong is a Fellow of the IEEE. He was founding chairman of the IEEE Control Systems Society Technical Committee on Discrete Event Systems and until recently served as an IEEE Control Systems Society Distinguished Lecturer. He has been on the editorial board of the IEEE Transactions on Automatic Control. He is currently on the editorial board of the journal Computer Networks. He has also served on the organizing committees of several international conferences. He has been on the program committees for the IEEE Conference on Decision and Control the American Control Conference the IEEE International Symposium on Intelligent Control IEEE Symposium on Computers and Communications and the IEEE Global Telecommunications Conference. He has also served in the executive committees for the IEEE Co
Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain tran...
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Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per-unit-time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max-min optimal policy, which maximizes the worst-case average per-unit-time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time-varying transition rates.
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