Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes
2001 ◽
Vol 14
◽
pp. 29-51
◽
Keyword(s):
Partially observable Markov decision processes (POMDPs) have recently become popular among many AI researchers because they serve as a natural model for planning under uncertainty. Value iteration is a well-known algorithm for finding optimal policies for POMDPs. It typically takes a large number of iterations to converge. This paper proposes a method for accelerating the convergence of value iteration. The method has been evaluated on an array of benchmark problems and was found to be very effective: It enabled value iteration to converge after only a few iterations on all the test problems.
2013 ◽
Vol 21
(06)
◽
pp. 821-863
◽
2009 ◽
pp. 237-246
◽
2020 ◽
Vol 282
(3)
◽
pp. 936-944