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.

翻译：近年来，基于强化学习的元黑箱优化（MetaBBO-RL）展现了在元层面利用强化学习来减少对底层黑箱优化器手动调参的强大能力。然而，该领域因缺乏统一基准而发展受阻。为填补这一空白，我们提出MetaBox——首个专为开发与评估MetaBBO-RL方法而设计的基准平台。MetaBox提供灵活的算法模板，使用户能够在该平台内轻松实现独特设计。此外，它涵盖从合成场景到真实场景的300余个问题实例，并集成包含传统黑箱优化器及最新MetaBBO-RL方法在内的19种基线方法库。更关键的是，MetaBox引入三项标准化性能指标，可对方法进行更全面的评估。为展示MetaBox在促进严谨评估与深度分析方面的实用价值，我们对现有MetaBBO-RL方法开展了广泛的基准测试研究。MetaBox已开源，访问地址为：https://github.com/GMC-DRL/MetaBox。