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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:GUO Qi,ZHANG Da-zhi,YANG Yong-tian.Study and application of reinforcement learning based on DAI in cooperative strategy of robot soccer[J].Journal of Harbin Institute Of Technology(New Series),2009,16(4):513-519.DOI:10.11916/j.issn.1005-9113.2009.04.014.
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Study and application of reinforcement learning based on DAI in cooperative strategy of robot soccer
Author NameAffiliation
GUO Qi Dept.of Mathematics,Harbin Institute of Technology,Harbin 150001,China
Dept.of Computer Science and Engineering,Harbin Engineering University,Harbin 150001,China 
ZHANG Da-zhi Dept.of Mathematics,Harbin Institute of Technology,Harbin 150001,China 
YANG Yong-tian Dept.of Computer Science and Engineering,Harbin Engineering University,Harbin 150001,China 
Abstract:
A dynamic cooperation model of multi-agent is established by combining reinforcement learning with distributed artificial intelligence(DAI),in which the concept of individual optimization loses its meaning because of the dependence of repayment on each agent itself and the choice of other agents.Utilizing the idea of DAI,the intellectual unit of each robot and the change of task and environment,each agent can make decisions independently and finish various complicated tasks by communication and reciprocation between each other.The method is superior to other reinforcement learning methods commonly used in the multi-agent system.It can improve the convergence velocity of reinforcement learning,decrease requirements of computer memory,and enhance the capability of computing and logical ratiocinating for agent.The result of a simulated robot soccer match proves that the proposed cooperative strategy is valid.
Key words:  robot soccer  reinforcement learning  cooperative strategy  distributed artificial intelligence
DOI:10.11916/j.issn.1005-9113.2009.04.014
Clc Number:TP242.6
Fund:

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