MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery

• URL: http://arxiv.org/abs/2603.20295v1
• Date: Thu, 19 Mar 2026 02:59:42 GMT
• Title: MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
• Abstract summary: We propose MARLIN, an efficient multi agent RL based approach for incremental DAG learning.<n>Experiments on synthetic and real datasets demonstrate that MARLIN outperforms state of the art methods in terms of both efficiency and effectiveness.
• Score: 50.18870781255611
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR [53.27792011950384]
  Reinforcement learning with verifiable rewards (RLVR) has emerged as a scalable paradigm for improving the reasoning capabilities of large language models.<n>We propose NudgeRL, a framework for structured and diversity-driven exploration in RLVR.<n>Our approach introduces Strategy Nudging, which conditions each rollout on lightweight, strategy-level contexts.
  arXiv  Detail & Related papers  (2026-05-15T08:22:59Z)
• Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration [56.074760766965085]
  PRISM achieves a dynamics-aware framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.<n>Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
  arXiv  Detail & Related papers  (2026-01-12T05:43:20Z)
• TIGER-MARL: Enhancing Multi-Agent Reinforcement Learning with Temporal Information through Graph-based Embeddings and Representations [7.269132496468141]
  We propose capturing and utilizing textitTemporal Information through Graph-based Embeddings and Representations or textbfTIGER to enhance multi-agent reinforcement learning (MARL)<n>TIGER constructs dynamic temporal graphs of MARL agents, connecting their current and historical interactions.<n>It then employs a temporal attention-based encoder to aggregate information across these structural and temporal neighborhoods.
  arXiv  Detail & Related papers  (2025-11-11T23:00:23Z)
• Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation [60.04281435591454]
  CRDA (Curriculum Reinforcement-Learning Data Augmentation) is a novel framework guiding detectors to progressively master multi-domain forgery features.<n>Central to our approach is integrating reinforcement learning and causal inference.<n>Our method significantly improves detector generalizability, outperforming SOTA methods across multiple cross-domain datasets.
  arXiv  Detail & Related papers  (2025-11-10T12:45:52Z)
• Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering [75.12322966980003]
  Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains.<n>Most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning.<n>Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering.<n>We propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA.
  arXiv  Detail & Related papers  (2025-06-11T12:03:52Z)
• Reinforcement Learning for Causal Discovery without Acyclicity Constraints [35.753289086012224]
  We introduce ALIAS, a novel approach to causal discovery powered by the reinforcement learning machinery.<n>Our method features an efficient policy for generating DAGs in just a single step with an optimal quadratic complexity.<n>We provide compelling empirical evidence for the strong performance of ALIAS in comparison with state-of-the-arts in causal discovery.
  arXiv  Detail & Related papers  (2024-08-24T03:12:21Z)
• Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints [20.45657219304883]
  This paper proposes a novel multi-agent reinforcement learning (MARL) method to learn multiple coordinated agents under directed acyclic graph (DAG) constraints. Unlike existing MARL approaches, our method explicitly exploits the DAG structure between agents to achieve more effective learning performance.
  arXiv  Detail & Related papers  (2023-07-13T13:41:24Z)
• A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning [15.544134849816528]
  We discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks. We propose a novel cross-modal knowledge distillation (CMKD) method for better supervised cross-modal learning (CML) performance. The proposed method enforces output features to be channel-wise independent and intermediate ones to be uniformly distributed, thereby learning semantically irrelevant features from the hard task to boost its accuracy.
  arXiv  Detail & Related papers  (2023-06-28T07:29:26Z)
• Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning [57.83232242068982]
  Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. It remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. This work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy.
  arXiv  Detail & Related papers  (2023-05-25T15:46:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.