The dynamics of generalized reinforcement learning

Ratul Lahkar () and Robert M. Seymour

Journal of Economic Theory, 2014, vol. 151, issue C, 584-595

Abstract: We consider reinforcement learning in games with both positive and negative payoffs. The Cross rule is the prototypical reinforcement learning rule in games that have only positive payoffs. We extend this rule to incorporate negative payoffs to obtain the generalized reinforcement learning rule. Applying this rule to a population game, we obtain the generalized reinforcement dynamic which describes the evolution of mixed strategies in the population. We apply the dynamic to the class of Rock–Scissor–Paper (RSP) games to establish local convergence to the interior rest point in all such games, including the bad RSP game.

Keywords: Reinforcement learning; Negative reinforcement; Replicator dynamic (search for similar items in EconPapers)
JEL-codes: C72 C73 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:151:y:2014:i:c:p:584-595

DOI: 10.1016/j.jet.2014.01.002

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