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Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization
We then demonstrate via rigorous analysis of the variance and by numerical comparisons on reinforcement learning tasks that the Gaussian sampling method used in [Salimans et al. 2016] is significantly inferior to the orthogonal sampling used in [Choromaski et al. 2018] as well as more general interpolation methods.
A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization
To this end, we use the results in [Berahas et al., 2019] and show how each method can satisfy the sufficient conditions, possibly only with some sufficiently large probability at each iteration, as happens to be the case with Gaussian smoothing and smoothing on a sphere.
Optimization and Control
