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Recently, Meta-Black-Box Optimization with Reinforcement Learning(MetaBBO-RL) has showcased the power of leveraging RL at the meta-level tomitigate manual fine-tuning of low-level black-box optimizers. However, thisfield is hindered by the lack of a unified benchmark. To fill this gap, weintroduce MetaBox, the first benchmark platform expressly tailored fordeveloping and evaluating MetaBBO-RL methods. MetaBox offers a flexiblealgorithmic template that allows users to effortlessly implement their uniquedesigns within the platform. Moreover, it provides a broad spectrum of over 300problem instances, collected from synthetic to realistic scenarios, and anextensive library of 19 baseline methods, including both traditional black-boxoptimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces threestandardized performance metrics, enabling a more thorough assessment of themethods. In a bid to illustrate the utility of MetaBox for facilitatingrigorous evaluation and in-depth analysis, we carry out a wide-rangingbenchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-sourceand accessible at: https://github.com/GMC-DRL/MetaBox.

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning