Reference : Relaxation schemes for min max generalization in deterministic batch mode reinforcement ...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/103489
Relaxation schemes for min max generalization in deterministic batch mode reinforcement learning
English
Fonteneau, Raphaël mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Ernst, Damien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Boigelot, Bernard mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Informatique >]
Louveaux, Quentin mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Système et modélisation : Optimisation discrète >]
Dec-2011
4th International NIPS Workshop on Optimization for Machine Learning (OPT 2011)
Yes
No
International
4th International NIPS Workshop on Optimization for Machine Learning (OPT 2011)
December 16th, 2011
Sierra Nevada
Spain
[en] Batch mode reinforcement learning ; Min max generalization ; Non-convex optimization
[en] We study the min max optimization problem introduced in [Fonteneau, 2011] for computing policies for batch mode reinforcement learning in a deterministic setting. This problem is NP-hard. We focus on the two-stage case for which we provide two relaxation schemes. The first relaxation scheme works by dropping some constraints in order to obtain a problem that is solvable in polynomial time. The second relaxation scheme, based on a Lagrangian relaxation where all constraints are dualized, leads to a conic quadratic programming problem. Both relaxation schemes are shown to provide better results than those given in [Fonteneau, 2011].
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers
http://hdl.handle.net/2268/103489

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