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In the paper VIME: Variational Information Maximizing Exploration, the authors suggest taking a single second-order step to efficiently optimize a variant of evidence lower bound

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They say

To optimize Eq. (12) efficiently, we only take a single second-order step. This way, the gradient is rescaled according to the curvature of the KL divergence at the origin. As such, we compute $D_{KL} [q(\theta; \phi + \lambda\Delta\phi)\Vert q(\theta; \phi)]$, with the update step $\Delta\phi$ defined as $$ \Delta\phi=H^{-1}(l)\nabla_\phi l(q(\theta;\phi),s_t) \tag{13} $$ in which $H(l)$ is the Hessian of $l(q(\theta;\phi),s_t)$.

I'm quite confused about how they obtain Eq.$(13)$: why would they multiply the gradient by the inverse of the Hessian?

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    $\begingroup$ This is Newton’s method! $\endgroup$
    – David M.
    Mar 9 '19 at 7:00
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I'm not familiar with the form of the cost function they are optimizing, but it seems to me that your question might be resolved by getting some intuition on second-order methods. See this wiki entry on a fundamental example and how second derivatives (i.e. Hessians) allow us to do better optimization by using curvature information of the function.

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  • $\begingroup$ I see, thank you so much! $\endgroup$
    – Maybe
    Mar 9 '19 at 10:11

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