Reinforcement Learning Rules in a Repeated Game
AbstractThis 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
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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 18 (2001)
Issue (Month): 1 (August)
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- Pietro Dindo & Jan Tuinstra, 2011.
"A Class of Evolutionary Models for Participation Games with Negative Feedback,"
Society for Computational Economics, vol. 37(3), pages 267-300, March.
- Pietro Dindo & Jan Tuinstra, 2010. "A class of evolutionary models for participation games with negative feedback," LEM Papers Series 2010/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- 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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