Reference : Variable selection for dynamic treatment regimes: a reinforcement learning approach

	Document type :	Scientific congresses and symposiums : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/13599

Title :	Variable selection for dynamic treatment regimes: a reinforcement learning approach
Language :	English
Author, co-author :	Fonteneau, Raphaël [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
	Wehenkel, Louis [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
	Ernst, Damien [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Publication date :	2008
Page number :	7
On invitation :	No
Audience :	International
Event name :	European Workshop on Reinforcement Learning 2008 (EWRL'08)
Event date :	30 June - 3 July
Event place (city) :	Villeneuve d'Ascq
Event country :	France
Keywords :	[en] dynamic treatment regimes ; reinforcement learning ; fitted Q iteration
Abstract :	[en] Dynamic treatment regimes (DTRs) can be inferred from data collected through some randomized clinical trials by using reinforcement learning algorithms. During these clinical trials, a large set of clinical indicators are usually monitored. However, it is often more convenient for clinicians to have DTRs which are only defined on a small set of indicators rather than on the original full set. To address this problem, we analyse the approximation architecture of the state-action value functions computed by the fitted Q iteration algorithm - a RL algorithm - using tree-based regressors in order to identify a small subset of relevant ones. The RL algorithm is then rerun by considering only as state variables these most relevant indicators to have DTRs defined on a small set of indicators. The approach is validated on benchmark problems inspired from the classical ‘car on the hill’ problem and the results obtained are positive.
Funders :	Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Permalink :	http://hdl.handle.net/2268/13599

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