Reference : Planning under uncertainty, ensembles of disturbance trees and kernelized discrete ac...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/14318
Planning under uncertainty, ensembles of disturbance trees and kernelized discrete action spaces
English
Defourny, Boris [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) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2009
Proceedings of the IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-09)
145-152
Yes
No
International
978-1-4244-2761-1
IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-09)
March 30 - April 2, 2009
Nashville
USA
[en] planning under uncertainty ; reinforcement learning ; multi-stage stochastic programming
[fr] disturbance trees
[en] Optimizing decisions on an ensemble of incomplete disturbance trees and aggregating their first stage decisions has been shown as a promising approach to (model-based) planning under uncertainty in large continuous action spaces and in small discrete ones. The present paper extends this approach and deals with large but highly structured action spaces, through a kernel-based aggregation scheme. The technique is applied to a test problem with a discrete action space of 6561 elements adapted from the NIPS 2005 SensorNetwork benchmark.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
http://hdl.handle.net/2268/14318
10.1109/ADPRL.2009.4927538

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