A Cooperation Model Using Reinforcement Learning for Multi-agent

  • Malrey Lee
  • Jaedeuk Lee
  • Hye-Jin Jeong
  • YoungSoon Lee
  • Seongman Choi
  • Thomas M. Gatton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3984)


In multi-agent systems, the common goals of each agent are established and the problems are solved through cooperation and control among agents. Because each agent performs parallel processes in a multi-agent system, this approach can be easily applied to problems requiring parallel processing. The parallel processing prevents system performance degradation due to local error operation in the system. It also can reduce the solution time when the problem is divided into several sub-problems. In this case, each agent is designed independently providing a relatively simple programming model for solution of the problem. Further, the system can be easily expanded by adding new function agents. In the study of multi-agent systems, the main research topic is the coordination and cooperation among agents.


Reinforcement Learning Virtual World Reinforcement Signal Head Direction Role Coordination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Malrey Lee
    • 1
  • Jaedeuk Lee
    • 2
  • Hye-Jin Jeong
    • 1
  • YoungSoon Lee
    • 1
  • Seongman Choi
    • 1
  • Thomas M. Gatton
    • 3
  1. 1.School of Electronics & Information EngineeringChonBuk National UniversityChonBukKorea
  2. 2.Chosun College of Science & TechnologyKorea
  3. 3.School of Engineering and TechnologyNational UniversityLa JollaUSA

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