Article (Scientific journals)
Model predictive control and reinforcement learning as two complementary frameworks
Ernst, Damien; Glavic, Mevludin; Capitanescu, Florin et al.
2007In International Journal of Tomography and Statistics, 6, p. 122-127
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Keywords :
model predictive control; reinforcement learning; interior point method; fitted Q iteration
Abstract :
[en] Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulations on a benchmark control problem taken from the power system literature and discuss the results obtained.
Disciplines :
Computer science
Author, co-author :
Ernst, Damien  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Glavic, Mevludin  
Capitanescu, Florin ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Model predictive control and reinforcement learning as two complementary frameworks
Publication date :
2007
Journal title :
International Journal of Tomography and Statistics
ISSN :
0972-9976
eISSN :
0973-7294
Publisher :
Indian Society for Development and Environment Research
Volume :
6
Pages :
122-127
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique
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