{
  "id": "1102.3508",
  "version": "v1",
  "published": "2011-02-17T07:08:37.000Z",
  "updated": "2011-02-17T07:08:37.000Z",
  "title": "Online Learning of Rested and Restless Bandits",
  "authors": [
    "Cem Tekin",
    "Mingyan Liu"
  ],
  "categories": [
    "math.OC",
    "cs.LG"
  ],
  "abstract": "In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K finite-state discrete-time Markov chains (arms) with unknown state spaces and statistics. At each time step the player can play M arms. The objective of the user is to decide for each step which M of the K arms to play over a sequence of trials so as to maximize its long term reward. The restless multiarmed bandit is particularly relevant to the application of opportunistic spectrum access (OSA), where a (secondary) user has access to a set of K channels, each of time-varying condition as a result of random fading and/or certain primary users' activities.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2011-02-17T07:08:37.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "restless bandits",
      "finite-state discrete-time markov chains",
      "restless multiarmed bandit",
      "unknown state spaces",
      "opportunistic spectrum access"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2011arXiv1102.3508T"
    }
  }
}