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Do Differentiable Simulators Give Better Policy Gradients?

ICML 2022 · Optimization › Optimizers · approach: Randomized gradient estimation · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use first-order rather than zeroth-order gradientsPushing trajectory optimization with soft springs (k=10) · Pushingoptimization performanceHelped
Use zeroth-order rather than first-order gradientsPushing trajectory optimization with stiff springs (k=1000) · Pushingoptimization performanceHelped
Use robust interpolation between first-order and zeroth-order gradientsPushing trajectory optimization · Pushingoptimization performanceHelped
Use first-order rather than zeroth-order gradientsFriction trajectory optimization · Frictionoptimization performanceMixed
Use robust interpolation between first-order and zeroth-order gradientsFriction trajectory optimization · Frictionconvergence speedHelped
Use zeroth-order rather than first-order gradientsTennis policy optimization · Tennispolicy performance across initial conditionsHelped
Use robust interpolation between first-order and zeroth-order gradientsTennis policy optimization · Tennisconvergence speedHelped
Use zeroth-order or interpolated gradients rather than first-order gradientsBall with wall landscape optimization · Ball with wallconvergence to a minimumHelped
Use zeroth-order or interpolated gradients rather than first-order gradientsAngular momentum transfer landscape optimization · Angular momentum transferconvergence to a robust minimumHelped
Use first-order gradients in a stiff or chaotic systemPolicy-gradient estimation · Stiff dynamics and chaotic systemsgradient-estimator varianceHurt
Choose the interpolation weight using empirical variance aloneGradient-estimator interpolation · Nearly or strictly discontinuous landscapesgradient-estimate accuracyHurt
Constrain interpolation to maintain accuracy while minimizing estimated varianceAoBG · Nearly or strictly discontinuous landscapesgradient-estimate accuracyHelped

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