Reference : Reinforcement learning versus model predictive control: a comparison on a power system p...
Scientific journals : Article
Engineering, computing & technology : Computer science
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/13602
Reinforcement learning versus model predictive control: a comparison on a power system problem
English
Ernst, Damien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Glavic, Mevludin [ > > ]
Capitanescu, Florin mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2009
IEEE Transactions on Systems, Man & Cybernetics : Part B
IEEE
33
2
517-519
Yes (verified by ORBi)
International
1083-4419
[en] approximate dynamic programming ; electric power oscillations damping ; fitted Q iteration ; interior point method ; model predictive control ; reinforcement learning ; tree-based supervised learning
[en] This paper compares reinforcement learning (RL) with model predictive control (MPC) in a unified framework and reports experimental results of their application to the synthesis of a controller for a nonlinear and deterministic electrical power oscillations damping problem. Both families of methods are based on the formulation of the control problem as a discrete-time optimal control problem. The considered MPC approach exploits an analytical model of the system dynamics and cost function and computes open-loop policies by applying an interior-point solver to a minimization problem in which the system dynamics are represented by equality constraints. The considered RL approach infers in a model-free way closed-loop policies from a set of system trajectories and instantaneous cost values by solving a sequence of batch-mode supervised learning problems. The results obtained provide insight into the pros and cons of the two approaches and show that RL may certainly be competitive with MPC even in contexts where a good deterministic system model is available.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
http://hdl.handle.net/2268/13602
10.1109/TSMCB.2008.2007630
http://www.montefiore.ulg.ac.be/~ernst/

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