reinforcement learning; policy evaluation; Monte Carlo estimation
Abstract :
[en] We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along a set of “broken trajectories” made of one-step transitions selected from the sample on the basis of the control policy. Under some Lipschitz continuity assumptions on the system dynamics, reward function and control policy, we provide bounds on the bias and variance of the estimator that depend only on the Lipschitz constants, on the number of broken trajectories used in the estimator, and on the sparsity of the sample of one-step transitions.
Disciplines :
Computer science
Author, co-author :
Fonteneau, Raphaël ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Murphy, Susan
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Ernst, Damien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Model-free Monte Carlo–like policy evaluation
Publication date :
May 2010
Event name :
Conférence Francophone sur l'Apprentissage Automatique (CAp) 2010
Event place :
Clermont-Ferrand, France
Event date :
17-19 May 2010
Main work title :
Proceedings of Conférence Francophone sur l'Apprentissage Automatique (CAp) 2010
Peer reviewed :
Peer reviewed
Funders :
FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture F.R.S.-FNRS - Fonds de la Recherche Scientifique
Commentary :
This paper won the "Best student paper award". It was also presented at AISTATS 2010 (see http://hdl.handle.net/2268/22651 )
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