TY - JOUR
T1 - Block-successive approximation for a discounted Markov decision model
AU - Haviv, Moshe
PY - 1985/2
Y1 - 1985/2
N2 - In this paper we suggest a new successive approximation method to compute the optimal discounted reward for finite state and action, discrete time, discounted Markov decision chains. The method is based on a block partitioning of the (stochastic) matrices corresponding to the stationary policies. The method is particularly attractive when the transition matrices are jointly nearly decomposable or nearly completely decomposable.
AB - In this paper we suggest a new successive approximation method to compute the optimal discounted reward for finite state and action, discrete time, discounted Markov decision chains. The method is based on a block partitioning of the (stochastic) matrices corresponding to the stationary policies. The method is particularly attractive when the transition matrices are jointly nearly decomposable or nearly completely decomposable.
KW - Markov decision model
KW - optimal reward
KW - partitioning transition matrices
KW - stationary policies
KW - successive approximation
UR - http://www.scopus.com/inward/record.url?scp=46549100290&partnerID=8YFLogxK
U2 - 10.1016/0304-4149(85)90046-8
DO - 10.1016/0304-4149(85)90046-8
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AN - SCOPUS:46549100290
SN - 0304-4149
VL - 19
SP - 151
EP - 160
JO - Stochastic Processes and their Applications
JF - Stochastic Processes and their Applications
IS - 1
ER -