Model-Based Reinforcement Learning in Multiagent Systems with Sequential Action Selection-Reference-Cited by-同舟云学术

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Model-Based Reinforcement Learning in Multiagent Systems with Sequential Action Selection

Published:2011 Issue:2 Volume:E94-D Page:255-263
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

AKRAMIZADEH Ali¹,AFSHAR Ahmad¹,MENHAJ Mohammad Bagher¹,JAFARI Samira¹

Affiliation:

1. Dept. EE., Computational Intelligence and Large Scale System Lab., Amir Kabir University of Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

http://www.jstage.jst.go.jp/article/transinf/E94.D/2/E94.D_2_255/_pdf

Reference38 articles.

1. [1] L. Panait and S. Luke, “Cooperative multi-agent learning: The state of the art,” Autonmous Agents Multi-Agent Systems, vol.11, no.3, pp.387-434, 2005.

2. A cooperative coevolutionary approach to function optimization

3. [3] S.G. Ficici and J.B. Pollack, “A game-theoretic approach to the simple coevolutionary algorithm,” Proc. Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI), Springer Verlag, 2000.

4. A Comprehensive Survey of Multiagent Reinforcement Learning

5. [5] R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Solving average cost Markov decision processes by means of a two-phase time aggregation algorithm;European Journal of Operational Research;2015-02

2. Accelerating the convergence of value iteration by using partial transition functions;European Journal of Operational Research;2013-08

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