提出可实时控碳且公平的海上物流多智能体强化学习框架
EcoFair-CH-MARL: Scalable Constrained Hierarchical Multi-Agent RL with Real-Time Emission Budgets and Fairness Guarantees
- 分层架构解耦战略与实时控制，支持线性扩展
- 在16港50船场景下减排15%、吞吐量提12%、公平性增45%
- 理论证明约束违规和公平损失均随时间平方根增长
全球减碳目标与市场压力要求海事物流兼具效率、可持续性与公平性。本文提出EcoFair-CH-MARL，一种约束型分层多智能体强化学习框架，融合三项创新：(i) 原始-对偶预算层，在随机天气与需求下严格控制累积排放；(ii) 兼顾公平性的奖励变换器，通过动态调度惩罚实现异构船队间的最大最小成本公平；(iii) 两级策略架构，分离战略路径规划与实时船舶控制，支持智能体数量线性扩展。新理论结果表明，约束违反与公平损失的后悔值均为O(√T)。在基于自动识别系统数据的高保真海事数字孪生（16港口、50艘船）及能源电网案例中，相比最先进分层与约束型MARL基线，最多降低15%排放、提升12%吞吐量、公平成本改善45%。该方法在公平性（更低吉尼系数、更高极小最大福利）上优于专门公平性算法（如SOTO、FEN），且模块化设计兼容策略与价值型学习器。因此，EcoFair-CH-MARL推动了安全关键领域大规模、合规、负责任多智能体协同的可行性。
原文摘要 · Abstract (English)
Global decarbonisation targets and tightening market pressures demand maritime logistics solutions that are simultaneously efficient, sustainable, and equitable. We introduce EcoFair-CH-MARL, a constrained hierarchical multi-agent reinforcement learning framework that unifies three innovations: (i) a primal-dual budget layer that provably bounds cumulative emissions under stochastic weather and demand; (ii) a fairness-aware reward transformer with dynamically scheduled penalties that enforces max-min cost equity across heterogeneous fleets; and (iii) a two-tier policy architecture that decouples strategic routing from real-time vessel control, enabling linear scaling in agent count. New theoretical results establish O(\sqrt{T}) regret for both constraint violations and fairness loss. Experiments on a high-fidelity maritime digital twin (16 ports, 50 vessels) driven by automatic identification system traces, plus an energy-grid case study, show up to 15% lower emissions, 12% higher through-put, and a 45% fair-cost improvement over state-of-the-art hierarchical and constrained MARL baselines. In addition, EcoFair-CH-MARL achieves stronger equity (lower Gini and higher min-max welfare) than fairness-specific MARL baselines (e.g., SOTO, FEN), and its modular design is compatible with both policy- and value-based learners. EcoFair-CH-MARL therefore advances the feasibility of large-scale, regulation-compliant, and socially responsible multi-agent coordination in safety-critical domains.
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