MAGNNET：基于多智能体图神经网络并采用深度强化学习的自动驾驶车辆高效任务分配
MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning
AI总结：
提出MAGNNET框架，融合GNN、CTDE与定制PPO实现UAV/UGV去中心化任务分配，在冲突规避、可扩展性和动态任务响应上表现优异。
摘要：
本文解决在通信约束下运行的异构多智能体系统中的去中心化任务分配挑战。我们提出了一种新颖框架，将图神经网络（GNN）与集中式训练、去中心化执行（CTDE）范式相结合，并通过为多智能体深度强化学习（MARL）定制的近端策略优化（PPO）算法进一步增强。该方法使无人机（UAV）和无人地面车辆（UGV）能够在三维网格环境中无需中央协调即可动态、高效地分配任务。该框架在最小化总行驶时间的同时避免任务分配冲突。在成本计算和路径规划方面，我们采用基于预留的 A* 和 R* 路径规划器。实验结果表明，该方法实现了高达92.5%的无冲突成功率，与集中式 Hungarian 方法相比仅有7.49%的性能差距，同时优于基于贪心方法的启发式去中心化基线。此外，该框架在多达20个智能体时表现出可扩展性，分配处理时间为2.8秒，并在响应动态生成任务方面具有鲁棒性，凸显其在复杂多智能体场景中实际应用的潜力。
英文摘要：
This paper addresses the challenge of decentralized task allocation within heterogeneous multi-agent systems operating under communication constraints. We introduce a novel framework that integrates graph neural networks (GNNs) with a centralized training and decentralized execution (CTDE) paradigm, further enhanced by a tailored Proximal Policy Optimization (PPO) algorithm for multi-agent deep reinforcement learning (MARL). Our approach enables unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to dynamically allocate tasks efficiently without necessitating central coordination in a 3D grid environment. The framework minimizes total travel time while simultaneously avoiding conflicts in task assignments. For the cost calculation and routing, we employ reservation-based A* and R* path planners. Experimental results revealed that our method achieves a high 92.5% conflict-free success rate, with only a 7.49% performance gap compared to the centralized Hungarian method, while outperforming the heuristic decentralized baseline based on greedy approach. Additionally, the framework exhibits scalability with up to 20 agents with allocation processing of 2.8 s and robustness in responding to dynamically generated tasks, underscoring its potential for real-world applications in complex multi-agent scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
