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.

通过学习主动学习策略和使用代理模型的梯度从而使用梯度下降法优化模拟参数，本文引入了一种新的方法来解决类似黑盒优化问题的一类问题。经过训练后，相对于局部代理模型方法、数值优化和贝叶斯方法，基于黑盒模拟器的问题的下游优化仅需要约 90％较少的昂贵模拟调用。