{
  "id": "1512.07669",
  "version": "v1",
  "published": "2015-12-23T23:59:32.000Z",
  "updated": "2015-12-23T23:59:32.000Z",
  "title": "Reinforcement Learning: Stochastic Approximation Algorithms for Markov Decision Processes",
  "authors": [
    "Vikram Krishnamurthy"
  ],
  "categories": [
    "math.OC"
  ],
  "abstract": "This article presents a short and concise description of stochastic approximation algorithms in reinforcement learning of Markov decision processes. The algorithms can also be used as a suboptimal method for partially observed Markov decision processes.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2015-12-23T23:59:32.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "markov decision processes",
      "stochastic approximation algorithms",
      "reinforcement learning",
      "concise description",
      "suboptimal method"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2015arXiv151207669K"
    }
  }
}