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Measurement of Underlying Cooperation in Multiagent Reinforcement Learning

  • Sachiyo Arai
  • Yoshihisa Ishigaki
  • Hironori Hirata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5357)

Abstract

Although a large number of algorithms have been proposed for generating cooperative behaviors, the question of how to evaluate mutual benefit among them is still open. This study provides a measure for cooperation degree among the reinforcement learning agents. By means of our proposed measure, that is based on information theory, the degree of interaction among agents can be evaluated from the viewpoint of information sharing. Here, we show the availability of this measure through some experiments on “pursuit game”, and evaluate the degree of cooperation among hunters and prey.

Keywords

Mutual Information Cooperative Behavior Game Theoretic Approach Pursuit Problem Pursuit Game 
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 2008

Authors and Affiliations

  • Sachiyo Arai
    • 1
  • Yoshihisa Ishigaki
    • 1
  • Hironori Hirata
    • 1
  1. 1.Graduate School of EngineeringChiba UniversityChibaJapan

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