title: Spike-based decision learning of Nash equilibria in two-player games.
abstract: Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.
authors.affiliation: Department of Physiology and Center for Cognition, Learning and Memory, University of Bern, Switzerland.
authors.forename: Johannes
authors.initials: J
authors.lastname: Friedrich
authors.forename: Walter
authors.initials: W
authors.lastname: Senn
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003163:Competitive Behavior / Q000502:physiology*; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D005716:Game Theory; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*