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A Momentum-Based Approach to Learning Nash Equilibria

  • Huaxiang Zhang
  • Peide Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4088)

Abstract

Learning a Nash equilibrium of a game is challengeable, and the issue of learning all the Nash equilibria seems intractable. This paper investigates the effectiveness of a momentum-based approach to learning the Nash equilibria of a game. Experimental results show the proposed algorithm can learn a Nash equilibrium in each learning iteration for a normal form strategic game. By employing a deflection technique, it can learn almost all the existing Nash equilibria of a game.

Keywords

Nash Equilibrium Pure Strategy Stochastic Game Strategic Game Matrix 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 2006

Authors and Affiliations

  • Huaxiang Zhang
    • 1
  • Peide Liu
    • 2
  1. 1.Dept. of Computer ScienceShandong Normal UniversityJinanChina
  2. 2.Dept. of Computer ScienceShandong Economics UniversityJinanChina

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