A Note on Strategic Learning in Policy Space

Steven O. Kimbrough (), Ming Lu () and Ann Kuo ()
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Steven O. Kimbrough: University of Pennsylvania
Ming Lu: University of Pennsylvania
Ann Kuo: University of Pennsylvania

A chapter in Formal Modelling in Electronic Commerce, 2005, pp 463-475 from Springer

Abstract: Abstract We report on a series of computational experiments with artificial agents learning in the context of games. Two kinds of learning are investigated: (1) a simple form of associative learning, called Q-learning, which occurs in state space, and (2) a simple form of learning, which we introduce here, that occurs in policy space. We compare the two methods on a number of repeated 2×2 games. We conclude that learning in policy space is an effective and promising method for learning in games.

Keywords: Nash Equilibrium; Reinforcement Learning; Repeated Game; Policy Space; Previous Round (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ihichp:978-3-540-26989-2_18

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DOI: 10.1007/3-540-26989-4_18

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