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.

最近，利用强化学习在元级别上提升元黑盒优化器的能力的MetaBBO-RL已经展示出了强大的潜力，然而该领域因缺乏统一的基准而受限。为了填补这一空白，我们引入了第一个专门为开发和评估MetaBBO-RL方法而量身定制的基准平台MetaBox。MetaBox提供了一个灵活的算法模板，允许用户在该平台内轻松实施其独特的设计。此外，它还提供了300多个问题实例，从合成到现实场景的广泛覆盖，并拥有19种基准方法的广泛库，包括传统黑盒优化器和最新的MetaBBO-RL方法。此外，MetaBox引入了三个标准化的性能指标，以便更全面地评估方法。为了展示MetaBox在促进严谨评估和深入分析方面的实用性，我们对现有的MetaBBO-RL方法进行了广泛的基准研究。我们的MetaBox是开源的，并且可以在这个https的URL上访问。

MetaBox: 具备增强学习的元黑盒优化的基准平台