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Planning under uncertainty, ensembles of disturbance trees and kernelized discrete action spaces

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3 Author(s)
Defourny, B. ; Dept. of Electr. Eng. & Comput. Sci., Univ. of Liege, Liege ; Ernst, D. ; Wehenkel, L.

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.

Published in:

Adaptive Dynamic Programming and Reinforcement Learning, 2009. ADPRL '09. IEEE Symposium on

Date of Conference:

March 30 2009-April 2 2009