{
  "id": 18435910,
  "title": "Permanent and Transient Representations for Continual Reinforcement Learning",
  "url": "https://urgent.news/2026/11/05/permanent-and-transient-representations-for-continual-reinforcement",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-05T02:11:47.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.07327v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Continual Reinforcement Learning (CRL) agents struggle to adapt to new situations while retaining prior knowledge, leading to a stability-plasticity trade-off. One approach to solving this problem is by using complementary learning systems: one for slow, long-term learning and another for quick, transient adaptation. Anand & Precup (2023) instantiated this idea using permanent and transient value…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}