DocumentCode
1314756
Title
Lebesgue-Sampling-Based Optimal Control Problems With Time Aggregation
Author
Xu, Yan-Kai ; Cao, Xi-Ren
Author_Institution
Beijing Geosci. Center, Schlumberger Ltd., Beijing, China
Volume
56
Issue
5
fYear
2011
fDate
5/1/2011 12:00:00 AM
Firstpage
1097
Lastpage
1109
Abstract
We formulate the Lebesgue-sampling-based optimal control problem. We show that the problem can be solved by the time aggregation approach in Markov decision processes (MDP) theory. Policy-iteration-based and reinforcement-learning-based methods are developed for the optimal policies. Both analytical solutions and sample-path-based algorithms are given. Compared to the periodic-sampling scheme, the Lebesgue sampling scheme improves system performance.
Keywords
Markov processes; iterative methods; learning (artificial intelligence); optimal control; Lebesgue-sampling-based optimal control problems; MDP theory; Markov decision processes; periodic-sampling scheme; policy-iteration-based methods; reinforcement-learning-based methods; sample-path-based algorithms; time aggregation; Boundary conditions; Cost function; Equations; Markov processes; Mathematical model; Optimal control; Aggregation; Markov decision processes (MDPs); performance potentials; reinforcement learning;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.2010.2073610
Filename
5565410
Link To Document