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Reinforcement Learning Rules in a Repeated Game


  • Bell, Ann Maria


This paper examines the performance of simple reinforcement learning algorithms in a stationary environment and in a repeated game where the environment evolves endogenously based on the actions of other agents. Some types of reinforcement learning rules can be extremely sensitive to small changes in the initial conditions, consequently, events early in a simulation can affect the performance of the rule over a relatively long time horizon. However, when multiple adaptive agents interact, algorithms that performed poorly in a stationary environment often converge rapidly to a stable aggregate behaviors despite the slow and erratic behavior of individual learners. Algorithms that are robust in stationary environments can exhibit slow convergence in an evolving environment. Copyright 2001 by Kluwer Academic Publishers

Suggested Citation

  • Bell, Ann Maria, 2001. "Reinforcement Learning Rules in a Repeated Game," Computational Economics, Springer;Society for Computational Economics, vol. 18(1), pages 89-110, August.
  • Handle: RePEc:kap:compec:v:18:y:2001:i:1:p:89-110

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    References listed on IDEAS

    1. Paarsch, Harry J., 1992. "Deciding between the common and private value paradigms in empirical models of auctions," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 191-215.
    2. Engelbrecht-Wiggans & Robert J. Weber, 1979. "On the Non-Existence of Multiplicative Equilibrium Bidding Strategies," Cowles Foundation Discussion Papers 523, Cowles Foundation for Research in Economics, Yale University.
    3. Laffont, Jean-Jacques & Vuong, Quang, 1993. "Structural econometric analysis of descending auctions," European Economic Review, Elsevier, vol. 37(2-3), pages 329-341, April.
    4. Milgrom, Paul, 1989. "Auctions and Bidding: A Primer," Journal of Economic Perspectives, American Economic Association, vol. 3(3), pages 3-22, Summer.
    5. Levin, Dan & Smith, James L, 1991. "Some Evidence on the Winner's Curse: Comment," American Economic Review, American Economic Association, vol. 81(1), pages 370-375, March.
    6. McAfee, R Preston & McMillan, John, 1987. "Auctions and Bidding," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 699-738, June.
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    Cited by:

    1. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    2. Pietro Dindo & Jan Tuinstra, 2011. "A Class of Evolutionary Models for Participation Games with Negative Feedback," Computational Economics, Springer;Society for Computational Economics, vol. 37(3), pages 267-300, March.
    3. Darmon, Eric & Waldeck, Roger, 2005. "Convergence of reinforcement learning to Nash equilibrium: A search-market experiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 119-130.
    4. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".

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