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 # MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
 
-MetaBox is 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. The github repos can be referred [here](https://github.com/GMC-DRL/MetaBox) and the paper can be found [here](https://openreview.net/forum?id=j2wasUypqN).
+MetaBox is 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. The github repos can be referred [here](https://github.com/GMC-DRL/MetaBox) and the paper can be found [here](https://arxiv.org/abs/2310.08252).
 
 
 ## Overview