Effects of noise on convergent game-learning dynamics

James B T Sanders, Tobias Galla, Jonathan L Shapiro
2012 Journal of Physics A: Mathematical and Theoretical  
We study stochastic effects on the lagging anchor dynamics, a reinforcement learning algorithm used to learn successful strategies in iterated games, which is known to converge to Nash points in the absence of noise. The dynamics is stochastic when players only have limited information about their opponents' strategic propensities. The effects of this noise are studied analytically in the case where it is small but finite, and we show that the statistics and correlation properties of
more » ... s can be computed to a high accuracy. We find that the system can exhibit quasicycles, driven by intrinsic noise. If players are asymmetric and use different parameters for their learning, a net payoff advantage can be achieved due to these stochastic oscillations around the deterministic equilibrium.
doi:10.1088/1751-8113/45/10/105001 fatcat:b2busqcbc5cqpgeqa7roukgca4