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Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games

NeurIPS 2019 · Optimization › Nonconvex and smooth optimization · approach: Equilibrium learning in zero-sum games · 17 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Apply continuous GDA from a safe initialization in a two-strategy hidden bilinear gameHidden bilinear games · Hidden bilinear games, two strategiestrajectory behaviorMixed
Apply continuous GDA from a safe initialization in a higher-dimensional hidden bilinear gameHidden bilinear games · Hidden bilinear games, more than two strategiestrajectory behaviorMixed
Allow local optima in the hidden functions that violate the safety conditionsContinuous GDA · Hidden bilinear gamesconvergence to spurious equilibriaHurt
Time-average the strategies and utilities along safe continuous-GDA trajectoriesContinuous GDA · Hidden bilinear games, safe initializationstime-averaged strategies and utilitiesHelped
Discretize GDA for sigmoid hidden functions from a safe initializationHidden bilinear games · Hidden bilinear games with sigmoid functions, safe initializationsenergy invariant HHurt
Allow a suitable learning rate and functions with local optima in discrete GDADiscrete GDA · Hidden bilinear gamesconvergence to spurious equilibriaHurt

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