DocumentCode
1213883
Title
A Policy Improvement Method in Constrained Stochastic Dynamic Programming
Author
Chang, Hyeong Soo
Author_Institution
Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul
Volume
51
Issue
9
fYear
2006
Firstpage
1523
Lastpage
1526
Abstract
This note presents a formal method of improving a given base-policy such that the performance of the resulting policy is no worse than that of the base-policy at all states in constrained stochastic dynamic programming. We consider finite horizon and discounted infinite horizon cases. The improvement method induces a policy iteration-type algorithm that converges to a local optimal policy
Keywords
Markov processes; dynamic programming; set theory; stochastic programming; constrained Markov decision process; constrained stochastic dynamic programming; discounted infinite horizon cases; finite horizon cases; policy iteration-type algorithm; Business; Cost function; Dynamic programming; Equations; Infinite horizon; Intelligent robots; Optimal control; Probability distribution; Random variables; Stochastic processes; Constrained Markov decision process; dynamic programming; policy improvement; policy iteration;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.2006.880801
Filename
1695995
Link To Document