@InProceedings{Supelec740,
author = {Hadrien Glaude and Fadi Akrimi and Matthieu Geist and Olivier Pietquin},
title = {A Non-Parametric Approach to Approximate Dynamic Programming},
year = {2011},
booktitle = {Proceedings of the 10th IEEE International Conference on Machine Learning and Applications (ICMLA 2011)},
pages = {317-322},
month = {December},
address = {Honolulu (USA)},
url = {http://www.metz.supelec.fr/metz/personnel/pietquin/pdf/ICMLA_2011_HGFAMGOP.pdf},
doi = {10.1109/ICMLA.2011.19},
abstract = {Approximate Dynamic Programming (ADP) is a machine learning method aiming at learning an optimal control policy for a dynamic and stochastic system from a logged set of observed interactions between the system and one or several non- optimal controlers. It defines a class of particular Reinforcement Learning (RL) algorithms which is a general paradigm for learning such a control policy from interactions. ADP addresses the problem of systems exhibiting a state space which is too large to be enumerated in the memory of a computer. Because of this, approximation schemes are used to generalize estimates over continuous state spaces. Nevertheless, RL still suffers from a lack of scalability to multidimensional continuous state spaces. In this paper, we propose the use of the Locally Weighted Projection Regression (LWPR) method to handle this scalability problem. We prove the efficacy of our approach on two standard benchmarks modified to exhibit larger state spaces.}
}