Efficient Approximation of Optimal Control for
Continuous-Time Markov Games
In IARCS Annual Conference on Foundations of Software
Technology and Theoretical Computer Science
, pages 399-410, Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik
, LIPIcs 13, 2011.
Downloads: pdf, bibURL: http://dx.doi.org/10.4230/LIPIcs.FSTTCS.2011.399
Abstract. We study the time-bounded reachability problem for continuous time Markov decision processes (CTMDPs) and games (CTMGs). Existing techniques for this problem use discretization techniques to break time into discrete intervals, and optimal control is approximated for each interval separately. Current techniques provide an accuracy of O(ε^2) on each interval, which leads to an infeasibly large number of intervals. We propose a sequence of approximations that achieve accuracies of O(ε^3), O(ε^4), and O(ε^5), that allow us to drastically reduce the number of intervals that are considered. For CTMDPs, the resulting algorithms are comparable to the heuristic approach given by Buckholz and Schulz, while also being theoretically justified. All of our results generalise to CTMGs, where our results yield the first practically implementable algorithms for this problem. We also provide positional strategies for both players that achieve similar error bounds.