Sequential Multi-Agent Dynamic Algorithm Configuration

• URL: http://arxiv.org/abs/2510.23535v1
• Date: Mon, 27 Oct 2025 17:11:03 GMT
• Title: Sequential Multi-Agent Dynamic Algorithm Configuration
• Authors: Chen Lu, Ke Xue, Lei Yuan, Yao Wang, Yaoyuan Wang, Sheng Fu, Chao Qian,
• Abstract summary: Dynamic algorithm configuration (DAC) is a recent trend in automated machine learning.<n>We propose the sequential multi-agent DAC (Seq-MADAC) framework to address this issue.<n> Experiments show Seq-MADAC's superior performance over state-of-the-art MARL methods.
• Score: 22.778404369668973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic algorithm configuration (DAC) is a recent trend in automated machine learning, which can dynamically adjust the algorithm's configuration during the execution process and relieve users from tedious trial-and-error tuning tasks. Recently, multi-agent reinforcement learning (MARL) approaches have improved the configuration of multiple heterogeneous hyperparameters, making various parameter configurations for complex algorithms possible. However, many complex algorithms have inherent inter-dependencies among multiple parameters (e.g., determining the operator type first and then the operator's parameter), which are, however, not considered in previous approaches, thus leading to sub-optimal results. In this paper, we propose the sequential multi-agent DAC (Seq-MADAC) framework to address this issue by considering the inherent inter-dependencies of multiple parameters. Specifically, we propose a sequential advantage decomposition network, which can leverage action-order information through sequential advantage decomposition. Experiments from synthetic functions to the configuration of multi-objective optimization algorithms demonstrate Seq-MADAC's superior performance over state-of-the-art MARL methods and show strong generalization across problem classes. Seq-MADAC establishes a new paradigm for the widespread dependency-aware automated algorithm configuration. Our code is available at https://github.com/lamda-bbo/seq-madac.

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