{
  "id": "1604.08153",
  "version": "v1",
  "published": "2016-04-27T17:48:39.000Z",
  "updated": "2016-04-27T17:48:39.000Z",
  "title": "Classifying Options for Deep Reinforcement Learning",
  "authors": [
    "Kai Arulkumaran",
    "Nat Dilokthanakul",
    "Murray Shanahan",
    "Anil Anthony Bharath"
  ],
  "categories": [
    "cs.LG",
    "cs.AI"
  ],
  "abstract": "Deep reinforcement learning is the learning of multiple levels of hierarchical representations for reinforcement learning. Hierarchical reinforcement learning focuses on temporal abstractions in planning and learning, allowing temporally-extended actions to be transferred between tasks. In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different \"option heads\" on the policy network, and a supervisory network for choosing between the different options. We show that in a domain where we have prior knowledge of the mapping between states and options, our augmented DQN achieves a policy competitive with that of a standard DQN, but with much lower sample complexity. This is achieved through a straightforward architectural adjustment to the DQN, as well as an additional supervised neural network.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-04-27T17:48:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep reinforcement learning",
      "classifying options",
      "lower sample complexity",
      "additional supervised neural network",
      "multiple levels"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2016arXiv160408153A"
    }
  }
}