文章基本信息

标题：Reinforcement Learning Dynamics in Social Dilemmas
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作者：Segismundo S. Izquierdo ; Luis R. Izquierdo ; Nicholas M. Gotts 等
期刊名称：Journal of Artificial Societies and Social Simulation
印刷版ISSN：1460-7425
出版年度：2008
卷号：11
期号：2
页码：1-22
出版社：University of Surrey, Department of Sociology
摘要：In this paper we replicate and advance Macy and Flache's (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players' decisions can change the dynamics of the model dramatically.
关键词：Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning