title: Reinforcement learning in complementarity game and population dynamics.
abstract: We systematically test and compare different reinforcement learning schemes in a complementarity game [J. Jost and W. Li, Physica A 345, 245 (2005)] played between members of two populations. More precisely, we study the Roth-Erev, Bush-Mosteller, and SoftMax reinforcement learning schemes. A modified version of Roth-Erev with a power exponent of 1.5, as opposed to 1 in the standard version, performs best. We also compare these reinforcement learning strategies with evolutionary schemes. This gives insight into aspects like the issue of quick adaptation as opposed to systematic exploration or the role of learning rates.
authors.affiliation: Max Planck Institute for Mathematics in the Sciences, Inselstr. 22, 04103 Leipzig, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Jost
authors.affiliation: Max Planck Institute for Mathematics in the Sciences, Inselstr. 22, 04103 Leipzig, Germany.
authors.forename: Wei
authors.initials: W
authors.lastname: Li
mesh_terms: D000222:Adaptation, Physiological; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D003661:Decision Support Techniques; D005716:Game Theory; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D011157:Population Dynamics; D012054:Reinforcement, Psychology