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A Note on Strategic Learning in Policy Space

In: Formal Modelling in Electronic Commerce

Author

Listed:
  • Steven O. Kimbrough

    (University of Pennsylvania)

  • Ming Lu

    (University of Pennsylvania)

  • Ann Kuo

    (University of Pennsylvania)

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.

Suggested Citation

  • Steven O. Kimbrough & Ming Lu & Ann Kuo, 2005. "A Note on Strategic Learning in Policy Space," International Handbooks on Information Systems, in: Steven O. Kimbrough & D.J. Wu (ed.), Formal Modelling in Electronic Commerce, pages 463-475, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-26989-2_18
    DOI: 10.1007/3-540-26989-4_18
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    Cited by:

    1. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 47-78, February.

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