{
  "id": 6682524,
  "title": "Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models",
  "url": "https://urgent.news/2026/09/10/distributed-optimization-of-modular-production-systems-using-model",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-10T14:30:52.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.11615v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "This paper presents a novel approach for data-driven self-learning control of highly flexible, modular manufacturing systems. Specifically, we employ a novel framework for model-based reinforcement learning which introduces approximate inverse process models within the training of reinforcement policies. This approach disentangles the learning of actuation dynamics and the dynamics in state…",
  "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."
}