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An Improved Multiagent Reinforcement Learning Algorithm
Compi?gne University of Technology, France September 19-September 22
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IAT.2005.422005 IEEE/WIC/ACM International Confe ...
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Xiangping Meng, Robert Babuska, Lucian Busoniu, Yu Chen, Wanyu Tan, "An Improved Multiagent Reinforcement Learning Algorithm," Intelligent Agent Technology, IEEE / WIC / ACM International Conference on, pp. 337-343, 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'05), 2005.
 
 
BibTex
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@article{ 10.1109/IAT.2005.42,
author = {Xiangping Meng and Robert Babuska and Lucian Busoniu and Yu Chen and Wanyu Tan},
title = {An Improved Multiagent Reinforcement Learning Algorithm},
journal ={Intelligent Agent Technology, IEEE / WIC / ACM International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2416-8},
pages = {337-343},
doi = {http://doi.ieeecomputersociety.org/10.1109/IAT.2005.42},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
TI - An Improved Multiagent Reinforcement Learning Algorithm
SN - 0-7695-2416-8
SP337
EP343
A1 - Xiangping Meng,
A1 - Robert Babuska,
A1 - Lucian Busoniu,
A1 - Yu Chen,
A1 - Wanyu Tan,
PY - 2005
KW - null
VL - 0
JA - Intelligent Agent Technology, IEEE / WIC / ACM International Conference on
ER -
 
Xiangping Meng, Department of Electrical Engineering, Changchun Institute of Technology, P. R. China
Robert Babuska, Delft Center for Systems and Control, Delft University of Technology, The Netherlands
Lucian Busoniu, Delft Center for Systems and Control, Delft University of Technology, The Netherlands
Yu Chen, Department of Computer Engineering,Northeast China Institute of Electric Power, P.R.China
Wanyu Tan, Department of Electrical Engineering, Changchun Institute of Technology, P. R. China

An improved reinforcement learning algorithm is proposed in this paper. This algorithm is based on linear programming method for finding the best-response policy. A pursuit example is tested and the results show that this algorithm has some properties, such as easy computation, simple operation procedure and can guarantee an good learning convergence.

Citation:
Xiangping Meng, Robert Babuska, Lucian Busoniu, Yu Chen, Wanyu Tan, "An Improved Multiagent Reinforcement Learning Algorithm," iat, pp.337-343, 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'05), 2005
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