  In recent years, solving optimization problems involving black-box simulators
has become a point of focus for the machine learning community due to their
ubiquity in science and engineering. The simulators describe a forward process
$f_{\mathrm{sim}}: (\psi, x) \rightarrow y$ from simulation parameters $\psi$
and input data $x$ to observations $y$, and the goal of the optimization
problem is to find parameters $\psi$ that minimize a desired loss function.
Sophisticated optimization algorithms typically require gradient information
regarding the forward process, $f_{\mathrm{sim}}$, with respect to the
parameters $\psi$. However, obtaining gradients from black-box simulators can
often be prohibitively expensive or, in some cases, impossible. Furthermore, in
many applications, practitioners aim to solve a set of related problems. Thus,
starting the optimization ``ab initio", i.e. from scratch, each time might be
inefficient if the forward model is expensive to evaluate. To address those
challenges, this paper introduces a novel method for solving classes of similar
black-box optimization problems by learning an active learning policy that
guides a differentiable surrogate's training and uses the surrogate's gradients
to optimize the simulation parameters with gradient descent. After training the
policy, downstream optimization of problems involving black-box simulators
requires up to $\sim$90\% fewer expensive simulator calls compared to baselines
such as local surrogate-based approaches, numerical optimization, and Bayesian
methods.
