Vero Chelu

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damped iteration of a nonexpansive operator will converge to one of its fixed poin ts
prox_algs.pdf
converge to a fixed point of T
prox_algs.pdf
α -averaged operators
prox_algs.pdf
o illustrate our theoretical results, we evaluate the two parameterizations by fitting value functions from data collected offline .
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf
referrable in situations where the residuals are expected to be highly correlated.
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf
his tells us that we can expect a notable benefit of using the implicit parameterization instead of the explicit one under balanced residuals
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf
r max /r min , of the largest and the smallest residual among the n data points is small, as is the case when residuals are similar to each other
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf
(variance)/(mean square) ratio
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf
ariance of the SGD updates compares to the magnitude of their expectatio
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf
ratio be as small as possible
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf
, by efficiently approximating the inverse Hessian thus resulting in updates approximating Netwon’s method.
NeurIPS-2021-understanding-end-to-end-model-based-reinforcement-learning-methods-as-implicit-parameterization-Supplemental.pdf