{
  "id": 4412,
  "url": "https://arxiv.org/abs/2605.13054v1",
  "title": "Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning",
  "summary": "Cross-domain offline reinforcement learning aims to adapt a policy from a source domain to a target domain using only pre-collected datasets, where environment dynamics may differ. A key challenge is to leverage source data while reducing distributional mismatch, particularly when the target dataset is extremely limited. To address this, we propose Target-aligned Coverage Expansion (TCE), a framework that decides how source data should be used, either by directly incorporating target-near transi",
  "authors": "Minung Kim, Jeongmo Kim, Gwanwoo Choi, Seungyul Han",
  "category": "research",
  "topics": "regulation,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-13T06:23:51.000Z",
  "fetched_at": "2026-07-14T16:30:59.236Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4412",
  "original_url": "https://arxiv.org/abs/2605.13054v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}