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

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

4 May 2025arXiv:2505.02010archive 2025-07-28

Zeyuan Ma, Zhiguang Cao, Zhou Jiang, Hongshu Guo, Yue-Jiao Gong

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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discreterize metaevo/q-mamba/components/operators.py official repository unverified MIT (permissive) · 6a7a074618eb707f · report
get_options metaevo/q-mamba/config.py official repository unverified MIT (permissive) · 640375203ce8920b · report
get_options metaevo/q-mamba/options.py official repository unverified MIT (permissive) · 66a1e55cf718accd · report
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rollout metaevo/q-mamba/EE_data_ratio.py official repository unverified MIT (permissive) · 25800a28407c2dab · report
selective_scan_ref identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · ba7b868fec181b57 · report
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Tasks

BenchmarkingMambaQ-Learning

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Methods

DACMambaQ-Learning

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