{
  "id": 3926,
  "url": "https://arxiv.org/abs/2605.22363v1",
  "title": "Incentive-Aligned Vehicle-to-Vehicle Energy Trading via Nash-Integrated Multi-Agent Reinforcement Learning",
  "summary": "Vehicle-to-vehicle (V2V) energy trading enables decentralized peer-to-peer energy exchange among electric vehicles (EVs), reducing grid dependency while monetizing surplus capacity. However, coordinating self-interested EV agents with diverse charging needs and uncertain arrival-departure schedules remains challenging. Existing approaches either require centralized optimization with computational limitations or lack fairness guarantees. This paper integrates Nash Bargaining Solution into Multi-A",
  "authors": "Yujin Lin, Yue Yang, Hao Wang",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-21T11:57:56.000Z",
  "fetched_at": "2026-07-14T16:30:36.743Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3926",
  "original_url": "https://arxiv.org/abs/2605.22363v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}