{
  "id": 7204,
  "url": "https://arxiv.org/abs/2603.14625v1",
  "title": "EcoFair-CH-MARL: Scalable Constrained Hierarchical Multi-Agent RL with Real-Time Emission Budgets and Fairness Guarantees",
  "summary": "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 ma",
  "authors": "Saad Alqithami",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-15T21:40:40.000Z",
  "fetched_at": "2026-07-14T16:33:03.573Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7204",
  "original_url": "https://arxiv.org/abs/2603.14625v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}