{
  "id": 252,
  "url": "https://arxiv.org/abs/2607.03703v1",
  "title": "Explainable Reinforcement Learning for Adaptive Traffic Signal Control",
  "summary": "Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical infrastructure like traffic control, the opaque, black-box nature of deep RL models poses challenges for transportation agency acceptance, regulatory compliance, operational trust, troubleshooting, and fine-tuning. To bridge this gap between high-performance optimization and human-comprehensible interpretability, this effort introduces a novel, explainable entity centri",
  "authors": "Dickens Kwesiga, Nishu Choudhary, Angshuman Guin, Michael Hunter",
  "category": "research",
  "topics": "regulation,safety-alignment,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-04T04:45:54.000Z",
  "fetched_at": "2026-07-14T14:14:24.247Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/252",
  "original_url": "https://arxiv.org/abs/2607.03703v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}