db403da6794af571a831 · Quality of Service Optimization in Mobile Edge Computing Networks via Deep Reinforcement Learning

{
  "title_en": "Quality of Service Optimization in Mobile Edge Computing Networks via Deep Reinforcement Learning",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2020-09-13",
  "date_original": "September 13, 2020",
  "url": "https://www.nist.gov/publications/quality-service-optimization-mobile-edge-computing-networks-deep-reinforcement-learning",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Li-Tse Hsieh, Hang Liu, Yang Guo , Robert Gazda",
  "abstract_excerpt_en": "Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q",
  "id": "db403da6794af571a831",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Quality of Service Optimization in Mobile Edge Computing Networks via Deep Reinforcement Learning",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2020-09-13",
      "date_original": "September 13, 2020",
      "url": "https://www.nist.gov/publications/quality-service-optimization-mobile-edge-computing-networks-deep-reinforcement-learning",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Li-Tse Hsieh, Hang Liu, Yang Guo , Robert Gazda",
      "abstract_excerpt_en": "Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q"
    }
  ],
  "translation_state": "none"
}