author = {Jérémy Fix and Matthieu Geist},
title = {Monte-Carlo Swarm Policy Search},
year = {2012},
booktitle = {Symposium on Swarm Intelligence and Differential Evolution},
publisher = {Springer Verlag - Heidelberg Berlin},
pages = {9 pages},
series = {Lecture Notes in Artificial Intelligence (LNAI)},
address = {Zakopane (Poland)},
url = {http://www.metz.supelec.fr//metz/personnel/geist_mat/pdfs/Supelec766.pdf},
abstract = {Finding optimal controllers of stochastic systems is a partic- ularly challenging problem tackled by the optimal control and reinforce- ment learning communities. A classic paradigm for handling such prob- lems is provided by Markov Decision Processes. However, the resulting underlying optimization problem is dicult to solve. In this paper, we explore the possible use of Particle Swarm Optimization to learn optimal controllers and show through some non-trivial experiments that it is a particularly promising lead.}