Absolutely expedient algorithms for learning Nash equilibria

Phansalkar, V. V. ; Sastry, P. S. ; Thathachar, M. A. L. (1994) Absolutely expedient algorithms for learning Nash equilibria Proceedings of the Indian Academy of Sciences - Mathematical Sciences, 104 (1). pp. 279-294. ISSN 0253-4142

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Official URL: http://www.ias.ac.in/j_archive/mathsci/104/1/279-2...

Related URL: http://dx.doi.org/10.1007/BF02830892

Abstract

This paper considers a multi-person discrete game with random payoffs. The distribution of the random payoff is unknown to the players and further none of the players know the strategies or the actual moves of other players. A class of absolutely expedient learning algorithms for the game based on a decentralised team of Learning Automata is presented. These algorithms correspond, in some sense, to rational behaviour on the part of the players. All stable stationary points of the algorithm are shown to be Nash equilibria for the game. It is also shown that under some additional constraints on the game, the team will always converge to a Nash equilibrium.

Item Type:Article
Source:Copyright of this article belongs to Indian Academy of Sciences.
Keywords:Nash Equilibria; Decentralised Learning Algorithm
ID Code:51321
Deposited On:28 Jul 2011 11:58
Last Modified:18 May 2016 05:19

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