多智能体无人机地面车协同任务分配，无中心化调度却高效避冲突。
MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning
- 用图神经网络+强化学习建模异构车辆，支持分布式决策
- 92.5%任务无冲突成功，仅比最优集中算法差7.49%
- 支持20个智能体实时分配，动态任务响应能力强
本文针对通信受限下异构多智能体系统的去中心化任务分配挑战，提出一种融合图神经网络（GNN）与中心化训练、去中心化执行（CTDE）范式的框架，并引入定制化近端策略优化（PPO）算法实现多智能体深度强化学习（MARL）。该方法使无人机（UAVs）与无人地面车（UGVs）在三维网格环境中无需中央协调即可动态高效分配任务，同时最小化总行程时间并避免任务冲突。路径规划采用基于预留的A*与R*算法。实验表明，该方法达到92.5%的无冲突成功率，性能仅比集中式匈牙利算法低7.49%，优于基于贪婪策略的启发式去中心化基线。此外，系统可扩展至20个智能体，任务分配耗时2.8秒，对动态生成任务具备鲁棒性，展现出在复杂多智能体场景中的实际应用潜力。
原文摘要 · Abstract (English)
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.
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