{
  "id": 873,
  "url": "https://arxiv.org/abs/2606.18132v1",
  "title": "Knowledge Reutilization in Meta-Reinforcement Learning",
  "summary": "Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control. This coupling can obscure non-parametric task semantics, reduce sample efficiency, and limit cross-agent reuse. We propose a meta-knowledge reutilization framework that learns task-level knowledge on a dynamics-simplified agent and transfers it to heterogeneous agents. The framework uses a Bayesian non",
  "authors": "Yuan Meng, Bo Wang, Juan de los Rios Ruiz, Xiangtong Yao, Zhenshan Bing, Fuchun Sun et al.",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": "meta",
  "regions": null,
  "published_at": "2026-06-16T16:32:28.000Z",
  "fetched_at": "2026-07-14T14:14:50.326Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/873",
  "original_url": "https://arxiv.org/abs/2606.18132v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}