{
  "id": "1811.12560",
  "version": "v1",
  "published": "2018-11-30T00:57:30.000Z",
  "updated": "2018-11-30T00:57:30.000Z",
  "title": "An Introduction to Deep Reinforcement Learning",
  "authors": [
    "Vincent Francois-Lavet",
    "Peter Henderson",
    "Riashat Islam",
    "Marc G. Bellemare",
    "Joelle Pineau"
  ],
  "comment": "Published in Foundations and Trend in Machine Learning",
  "journal": "Foundations and Trend in Machine Learning: Vol. 11, No. 3-4, 2018",
  "doi": "10.1561/2200000071",
  "categories": [
    "cs.LG",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-11-30T00:57:30.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "introduction",
      "deep rl opens",
      "deep reinforcement learning models",
      "basic machine learning concepts",
      "applications"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}