{
  "id": 4519,
  "url": "https://arxiv.org/abs/2605.18809v1",
  "title": "Metric-Gradient Projection for Stable Multi-Agent Policy Learning",
  "summary": "General-sum multi-agent learning is often governed by a stacked update field in which each agent's policy update changes the optimization landscape faced by the others. This coupling can entangle an integrable component of collective improvement with cyclic interaction dynamics, leading to slow or unstable multi-agent learning. Existing approaches, such as regularization, credit assignment, and consensus methods, stabilize MARL through local or algorithmic modifications; HPML complements them by",
  "authors": "Zuyuan Zhang, Sizhe Tang, Mahdi Imani, Tian Lan",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-12T01:02:01.000Z",
  "fetched_at": "2026-07-14T16:31:03.579Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4519",
  "original_url": "https://arxiv.org/abs/2605.18809v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}