Recently, neural networks trained as optimizers under the "learning to learn"
or meta-learning framework have been shown to be effective for a broad range of
optimization tasks including derivative-free black-box function optimization.
Recurrent neural networks (RNNs) trained to optimize a diverse set of synthetic
non-convex differentiable functions via gradient descent have been effective at
optimizing derivative-free black-box functions. In this work, we propose
RNN-Opt: an approach for learning RNN-based optimizers for optimizing
real-parameter single-objective continuous functions under limited budget
constraints. Existing approaches utilize an observed improvement based
meta-learning loss function for training such models. We propose training
RNN-Opt by using synthetic non-convex functions with known (approximate)
optimal values by directly using discounted regret as our meta-learning loss
function. We hypothesize that a regret-based loss function mimics typical
testing scenarios, and would therefore lead to better optimizers compared to
optimizers trained only to propose queries that improve over previous queries.
Further, RNN-Opt incorporates simple yet effective enhancements during training
and inference procedures to deal with the following practical challenges: i)
Unknown range of possible values for the black-box function to be optimized,
and ii) Practical and domain-knowledge based constraints on the input
parameters. We demonstrate the efficacy of RNN-Opt in comparison to existing
methods on several synthetic as well as standard benchmark black-box functions
along with an anonymized industrial constrained optimization problem.

本文提出了一种基于 RNN 的优化器 RNN-Opt，并使用合成非凸函数直接使用折扣遗憾作为元学习损失函数来训练，该方法可有效地优化具有限制预算的单目标连续函数。