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）展现了在元层次上利用强化学习来减少底层黑箱优化器手动调参的潜力。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——首个专为元黑箱优化方法开发与评估定制的基准平台。MetaBox提供灵活的算法模板，使用户能够在该平台中轻松实现个性化设计。此外，平台收录了从合成场景到实际场景的超300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。同时，MetaBox引入三项标准化性能指标，支持对方法进行更全面的评估。为验证MetaBox在促进严格评估与深度分析方面的实用性，我们对现有MetaBBO-RL方法开展了广泛的基准测试研究。本项目开源，访问地址：https://github.com/GMC-DRL/MetaBox。