{
  "id": 19545,
  "url": "https://link.springer.com/article/10.1007/s10462-026-11640-4",
  "title": "Reinforcement Learning and Model-based Planning in Practice: A Survey of Algorithmic Rationale and Domain Applications",
  "summary": "Reinforcement Learning (RL) is a foundational framework in Artificial Intelligence (AI) that enables agents to acquire optimal decision-making strategies through interactions with their environments. Building on principles of trial-and-error learning, RL adapts dynamically by leveraging feedback in the form of rewards or penalties. This paper provides a comprehensive survey of RL, its integration with Deep Learning into Deep Reinforcement Learning (DRL), and the emerging field of model-based pla",
  "authors": null,
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T00:00:00.000Z",
  "fetched_at": "2026-08-15T05:10:17.122Z",
  "source_slug": "x-artificial-intelligence-review",
  "source_name": "Artificial Intelligence Review",
  "source_homepage": "https://link.springer.com/journal/10462",
  "ethics_ai_record_url": "https://ethics.ai/record/19545",
  "original_url": "https://link.springer.com/article/10.1007/s10462-026-11640-4",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}