{
  "id": 1521,
  "url": "https://arxiv.org/abs/2607.10802",
  "title": "Q-Learning Lab: Teaching Reinforcement Learning Through Learner-Generated Trace Analysis",
  "summary": "arXiv:2607.10802v1 Announce Type: new Abstract: Reinforcement learning is usually introduced through the Bellman update, yet the equation often remains abstract to undergraduates: they watch policy arrows converge but rarely observe how each value is computed or why an action is chosen. We present Q-Learning Lab, a single-file, browser-based, bilingual (Thai/English) tool for teaching tabular Q-learning that requires no installation. Beyond the usual gridworld visualization - color-coded Q-value",
  "authors": "Ekkachai Jueng",
  "category": "research",
  "topics": "regulation",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-14T04:00:00.000Z",
  "fetched_at": "2026-07-14T16:04:12.223Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/1521",
  "original_url": "https://arxiv.org/abs/2607.10802",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}