The College of William and Mary, located in Williamsburg, Virginia, is one of the oldest and most distinguished public research universities in the United States, founded in 1693 under a royal charter. Known for its rigorous academics and strong liberal arts tradition, the university combines a close-knit learning environment with significant research activity across fields such as data science, computer science, and public policy. NEC Laboratories America collaborates with William and Mary researchers to advance work in artificial intelligence, machine learning, data science, and networked systems. Read our latest publications with The College of William and Mary.

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MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery

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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 out-performs state-of-the-art methods in terms of both efficiency and effectiveness.

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