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On the Convergence of Reinforcement Learning.

Abstract:

This paper examines the convergence of payoffs and strategies in Erev and Roth's model of reinforcement learning. When all players use this rule it eliminates iteratively dominated strategies and in two-person constant-sum games average payoffs converge to the value of the game. Strategies converge in constant-sum games with unique equilibria if they are pure or if they are mixed and the game is 2 x 2. The long-run behaviour of the learning rule is governed by equations related to Maynard Smi...

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Authors


A. W. Beggs More by this author
Volume:
96
Series:
Discussion Papers
Publication date:
2002
URN:
uuid:c890fa3f-de61-4ee6-96d7-e265acade350
Local pid:
ora:1133
Language:
English

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