Recently, Meta-Black-Box Optimization with Reinforcement Learning
(MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to
mitigate manual fine-tuning of low-level black-box optimizers. However, this
field is hindered by the lack of a unified benchmark. To fill this gap, we
introduce MetaBox, the first benchmark platform expressly tailored for
developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible
algorithmic template that allows users to effortlessly implement their unique
designs within the platform. Moreover, it provides a broad spectrum of over 300
problem instances, collected from synthetic to realistic scenarios, and an
extensive library of 19 baseline methods, including both traditional black-box
optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three
standardized performance metrics, enabling a more thorough assessment of the
methods. In a bid to illustrate the utility of MetaBox for facilitating
rigorous evaluation and in-depth analysis, we carry out a wide-ranging
benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source
and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202