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.
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Zhang, H., Liu, P. (2006). A Momentum-Based Approach to Learning Nash Equilibria. In: Shi, ZZ., Sadananda, R. (eds) Agent Computing and Multi-Agent Systems. PRIMA 2006. Lecture Notes in Computer Science(), vol 4088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802372_54
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DOI: https://doi.org/10.1007/11802372_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36707-9
Online ISBN: 978-3-540-36860-1
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