提出一种约束型分层强化学习框架，提升海运物流的环保与公平性。
CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics
- 分层决策结合动态约束与公平奖励，实时控制排放并均衡资源分配。
- 模拟实验显示碳排放显著降低，公平性和运营效率同步提升。
- 适用于有约束、动态变化的多智能体系统，具通用可扩展性。
应对全球温室气体排放和资源不平等等挑战，亟需智能体间高效协同。本文提出CH-MARL（约束型分层多智能体强化学习）框架，融合分层决策、动态约束执行与公平性感知奖励设计。该框架通过实时约束层确保全球排放上限合规，同时引入公平性度量促进智能体间资源分配均衡。在模拟海运物流环境中测试表明，该方法显著降低碳排放，同时提升公平性与运行效率。该方法不仅在特定场景表现优异，更提供了一种可扩展、通用的多智能体协同解决方案，推动强化学习在受限动态环境中的应用边界。
原文摘要 · Abstract (English)
Addressing global challenges such as greenhouse gas emissions and resource inequity demands advanced AI-driven coordination among autonomous agents. We propose CH-MARL (Constrained Hierarchical Multiagent Reinforcement Learning), a novel framework that integrates hierarchical decision-making with dynamic constraint enforcement and fairness-aware reward shaping. CH-MARL employs a real-time constraint-enforcement layer to ensure adherence to global emission caps, while incorporating fairness metrics that promote equitable resource distribution among agents. Experiments conducted in a simulated maritime logistics environment demonstrate considerable reductions in emissions, along with improvements in fairness and operational efficiency. Beyond this domain-specific success, CH-MARL provides a scalable, generalizable solution to multi-agent coordination challenges in constrained, dynamic settings, thus advancing the state of the art in reinforcement learning.
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