用强化学习与图神经网络优化任务分配，自动学习高效策略。
Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks
- 结合强化学习与图神经网络，从环境交互中自适应学习分配策略。
- 在合成与真实数据上优于基线算法，显著提升任务分配效率。
- 适合需要自动调度的复杂系统，如云计算与工业生产排程。
复杂调度中的高效任务分配在实际应用中面临巨大挑战。本文提出一种新颖方法，结合强化学习（RL）与图神经网络（GNN），解决任务分配问题（JAP）。JAP需在多种约束下将尽可能多的任务分配给可用资源。所提方法通过与环境试错交互学习自适应策略，并利用问题的图结构数据。相比监督学习，无需人工标注，突破其瓶颈。在合成数据与真实数据上的实验表明，该方法有效且具备良好泛化能力，优于基线算法，展现出在复杂调度中优化任务分配的巨大潜力。
原文摘要 · Abstract (English)
Efficient job allocation in complex scheduling problems poses significant challenges in real-world applications. In this report, we propose a novel approach that leverages the power of Reinforcement Learning (RL) and Graph Neural Networks (GNNs) to tackle the Job Allocation Problem (JAP). The JAP involves allocating a maximum set of jobs to available resources while considering several constraints. Our approach enables learning of adaptive policies through trial-and-error interactions with the environment while exploiting the graph-structured data of the problem. By leveraging RL, we eliminate the need for manual annotation, a major bottleneck in supervised learning approaches. Experimental evaluations on synthetic and real-world data demonstrate the effectiveness and generalizability of our proposed approach, outperforming baseline algorithms and showcasing its potential for optimizing job allocation in complex scheduling problems.
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