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A reinforcement learning process in extensive form games

Author

Listed:
  • Jean-François Laslier

    (CECO - Laboratoire d'économétrie de l'École polytechnique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

  • Bernard Walliser

    (CERAS - Centre d'enseignement et de recherche en analyse socio-économique - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)

Abstract

The CPR ("cumulative proportional reinforcement") learning rule stipulates that an agent chooses a move with a probability proportional to the cumulative payoff she obtained in the past with that move. Previously considered for strategies in normal form games (Laslier, Topol and Walliser, Games and Econ. Behav., 2001), the CPR rule is here adapted for actions in perfect information extensive form games. The paper shows that the action-based CPR process converges with probability one to the (unique) subgame perfect equilibrium.

Suggested Citation

  • Jean-François Laslier & Bernard Walliser, 2005. "A reinforcement learning process in extensive form games," Post-Print halshs-00754083, HAL.
    • Handle: RePEc:hal:journl:halshs-00754083
    DOI: 10.1007/s001820400194
    as

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    Cited by:

    1. Thorsten Chmura & Thomas Pitz, 2007. "An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Experimental Congestion Games," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-1.
    2. Ioannou, Christos A. & Romero, Julian, 2014. "A generalized approach to belief learning in repeated games," Games and Economic Behavior, Elsevier, vol. 87(C), pages 178-203.
    3. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
    4. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    5. Maxwell Pak & Bing Xu, 2016. "Generalized reinforcement learning in perfect-information games," International Journal of Game Theory, Springer;Game Theory Society, vol. 45(4), pages 985-1011, November.
    6. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.

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