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Drone Routing Problems (DRP) focus on finding optimal paths for autonomous drones in a graph-based environment, minimizing movement costs and avoiding collisions. DRP is modeled as a cooperative multi-agent problem, for which Multi-Agent Reinforcement Learning (MARL) offers a promising solution. However, MARL struggles with collision avoidance through trial and error and cannot guarantee collision-free operations. This paper proposes a safety control method for MARL, modifying unsafe actions by stopping those with high collision risks and allowing agents to yield routes. We implement Safe QMIX by integrating a safety control mechanism into QMIX and demonstrate its effectiveness through experimental evaluation, achieving lower collision rates and improved pathfinding efficiency.

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Safe Multi-agent Reinforcement Learning for Drone Routing Problems

  • 会议论文

  • 发表日期：

    2025年

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  • Masahiro Kaji,
  • Donghui Lin,
  • Fumito Uwano

摘要

Drone Routing Problems (DRP) focus on finding optimal paths for autonomous drones in a graph-based environment, minimizing movement costs and avoiding collisions. DRP is modeled as a cooperative multi-agent problem, for which Multi-Agent Reinforcement Learning (MARL) offers a promising solution. However, MARL struggles with collision avoidance through trial and error and cannot guarantee collision-free operations. This paper proposes a safety control method for MARL, modifying unsafe actions by stopping those with high collision risks and allowing agents to yield routes. We implement Safe QMIX by integrating a safety control mechanism into QMIX and demonstrate its effectiveness through experimental evaluation, achieving lower collision rates and improved pathfinding efficiency.