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Spike-based decision learning of Nash equilibria in two-player games.

  • Friedrich J Department of Physiology and Center for Cognition, Learning and Memory, University of Bern, Switzerland.
  • Senn W
  • 2012-10-03
Published in:
  • PLoS computational biology. - 2012
Action Potentials
Brain
Competitive Behavior
Computer Simulation
Decision Making
Game Theory
Humans
Models, Neurological
Nerve Net
English 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.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/6988
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