Meta-Black-Box-Optimization through Offline Q-function Learning

• URL: http://arxiv.org/abs/2505.02010v1
• Date: Sun, 04 May 2025 06:41:43 GMT
• Title: Meta-Black-Box-Optimization through Offline Q-function Learning
• Authors: Zeyuan Ma, Zhiguang Cao, Zhou Jiang, Hongshu Guo, Yue-Jiao Gong,
• Abstract summary: We propose an offline learning-based MetaBBO framework, termed Q-Mamba, to attain both effectiveness and efficiency in MetaBBO.<n>Under this setting, we propose three novel designs to meta-learn DAC policy from offline data.<n>We observe that Q-Mamba achieves competitive or even superior performance to prior online/offline baselines.
• Score: 17.565058993388707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in Meta-Black-Box-Optimization (MetaBBO) has demonstrated that using RL to learn a meta-level policy for dynamic algorithm configuration (DAC) over an optimization task distribution could significantly enhance the performance of the low-level BBO algorithm. However, the online learning paradigms in existing works makes the efficiency of MetaBBO problematic. To address this, we propose an offline learning-based MetaBBO framework in this paper, termed Q-Mamba, to attain both effectiveness and efficiency in MetaBBO. Specifically, we first transform DAC task into long-sequence decision process. This allows us further introduce an effective Q-function decomposition mechanism to reduce the learning difficulty within the intricate algorithm configuration space. Under this setting, we propose three novel designs to meta-learn DAC policy from offline data: we first propose a novel collection strategy for constructing offline DAC experiences dataset with balanced exploration and exploitation. We then establish a decomposition-based Q-loss that incorporates conservative Q-learning to promote stable offline learning from the offline dataset. To further improve the offline learning efficiency, we equip our work with a Mamba architecture which helps long-sequence learning effectiveness and efficiency by selective state model and hardware-aware parallel scan respectively. Through extensive benchmarking, we observe that Q-Mamba achieves competitive or even superior performance to prior online/offline baselines, while significantly improving the training efficiency of existing online baselines. We provide sourcecodes of Q-Mamba at https://github.com/MetaEvo/Q-Mamba.

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