{
  "id": 15223,
  "url": "https://arxiv.org/abs/2607.28408v1",
  "title": "On-Policy and Off-Policy Learning for Large Action Spaces",
  "summary": "This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback. The main framework is contextual bandits, with two paradigms: on-policy learning, where the agent interacts sequentially with the environment and minimizes regret, and off-policy learning, where it learns from logged data collected by a logging policy. In large action spaces, both settings face major challenges: inefficient explorat",
  "authors": "Imad Aouali",
  "category": "research",
  "topics": "regulation,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T15:56:11.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/15223",
  "original_url": "https://arxiv.org/abs/2607.28408v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}