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<p>Multi-Agent Path Finding (MAPF) in dynamic environments is challenging due to limited observations, non-cooperative moving obstacles, and inefficient inter-agent coordination. Existing search-based and reinforcement learning methods often struggle to balance real-time adaptability, collision avoidance, and scalable cooperation. To address these issues, this paper proposes MA-DyRoLT, a MAPPO-based MAPF framework that integrates dynamic waypoint generation and learned communication topology. The dynamic waypoint mechanism provides adaptive local guidance according to environmental openness, while the learned topology enables sparse and task-relevant information sharing among agents. In addition, a Transformer-based temporal encoder is introduced to capture historical observation dependencies, and an auxiliary pedestrian trajectory prediction module with a prediction-based risk penalty is designed to encourage proactive collision avoidance. Simulation results demonstrate that MA-DyRoLT outperforms representative search-based and learning-based baselines in success rate, movement time, and runtime. It maintains high success rates in large-scale dynamic scenarios, achieving 0.998 success rate in a 120 × 130 environment with 150 agents. These results verify the effectiveness, scalability, and real-time potential of the proposed method.</p>

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MA-DyRoLT: multi-agent path finding method based on dynamic waypoints and learning communication topology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Zongbao Liang,
  • Shichao Wang,
  • Xiaojun Yu,
  • Jiajun Liu,
  • Junli Feng,
  • Jingni Ma

摘要

Multi-Agent Path Finding (MAPF) in dynamic environments is challenging due to limited observations, non-cooperative moving obstacles, and inefficient inter-agent coordination. Existing search-based and reinforcement learning methods often struggle to balance real-time adaptability, collision avoidance, and scalable cooperation. To address these issues, this paper proposes MA-DyRoLT, a MAPPO-based MAPF framework that integrates dynamic waypoint generation and learned communication topology. The dynamic waypoint mechanism provides adaptive local guidance according to environmental openness, while the learned topology enables sparse and task-relevant information sharing among agents. In addition, a Transformer-based temporal encoder is introduced to capture historical observation dependencies, and an auxiliary pedestrian trajectory prediction module with a prediction-based risk penalty is designed to encourage proactive collision avoidance. Simulation results demonstrate that MA-DyRoLT outperforms representative search-based and learning-based baselines in success rate, movement time, and runtime. It maintains high success rates in large-scale dynamic scenarios, achieving 0.998 success rate in a 120 × 130 environment with 150 agents. These results verify the effectiveness, scalability, and real-time potential of the proposed method.