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• W1978046526 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." @default.
• W1978046526 created "2016-06-24" @default.
• W1978046526 creator A5033106713 @default.
• W1978046526 creator A5088167504 @default.
• W1978046526 date "2012-09-27" @default.
• W1978046526 modified "2023-09-28" @default.
• W1978046526 title "Spike-based Decision Learning of Nash Equilibria in Two-Player Games" @default.
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• W1978046526 doi "https://doi.org/10.1371/journal.pcbi.1002691" @default.
• W1978046526 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3459907" @default.
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• W1978046526 hasPublicationYear "2012" @default.
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