[2603.20295] MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery

Computer Science > Machine Learning arXiv:2603.20295 (cs) [Submitted on 19 Mar 2026] Title:MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery Authors:Dong Li, Zhengzhang Chen, Xujiang Zhao, Linlin Yu, Zhong Chen, Yi He, Haifeng Chen, Chen Zhao View a PDF of the paper titled MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery, by Dong Li and 7 other authors View PDF HTML (experimental) Abstract:Uncovering causal structures from observational data is crucial for understanding complex systems and making informed decisions. While reinforcement learning (RL) has shown promise in identifying these structures in the form of a directed acyclic graph (DAG), existing methods often lack efficiency, making them unsuitable for online applications. In this paper, we propose MARLIN, an efficient multi agent RL based approach for incremental DAG learning. MARLIN uses a DAG generation policy that maps a continuous real valued space to the DAG space as an intra batch strategy, then incorporates two RL agents state specific and state invariant to uncover causal relationships and integrates these agents into an incremental learning framework. Furthermore, the framework leverages a factored action space to enhance parallelization efficiency. Extensive experiments on synthetic and real datasets demonstrate that MARLIN outperforms state of the art methods in terms of both efficiency and effectiveness. Comments: Subjects: Machine Learning (cs.LG); Artifi...