  Black-Box Optimization (BBO) has found successful applications in many fields
of science and engineering. Recently, there has been a growing interest in
meta-learning particular components of BBO algorithms to speed up optimization
and get rid of tedious hand-crafted heuristics. As an extension, learning the
entire algorithm from data requires the least labor from experts and can
provide the most flexibility. In this paper, we propose RIBBO, a method to
reinforce-learn a BBO algorithm from offline data in an end-to-end fashion.
RIBBO employs expressive sequence models to learn the optimization histories
produced by multiple behavior algorithms and tasks, leveraging the in-context
learning ability of large models to extract task information and make decisions
accordingly. Central to our method is to augment the optimization histories
with \textit{regret-to-go} tokens, which are designed to represent the
performance of an algorithm based on cumulative regret over the future part of
the histories. The integration of regret-to-go tokens enables RIBBO to
automatically generate sequences of query points that satisfy the user-desired
regret, which is verified by its universally good empirical performance on
diverse problems, including BBO benchmark functions, hyper-parameter
optimization and robot control problems.
