{
  "id": 16129,
  "url": "https://arxiv.org/abs/2608.01745v1",
  "title": "Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints",
  "summary": "Maximizing throughput under proportional fairness in dense wireless networks requires jointly managing user association, scheduling, base station (BS) activation, and handover control under hard finite-horizon energy and handover budgets, which induces a fundamental tension between BS-side energy management and user-side handover regulation. While multi-agent reinforcement learning (MARL) is a natural framework for such distributed sequential control, its application here faces two difficulties:",
  "authors": "Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong, Songnam Hong",
  "category": "research",
  "topics": "bias-fairness,regulation,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T06:11:38.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/16129",
  "original_url": "https://arxiv.org/abs/2608.01745v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}