{
  "id": "1902.04118",
  "version": "v1",
  "published": "2019-02-11T19:59:23.000Z",
  "updated": "2019-02-11T19:59:23.000Z",
  "title": "WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving",
  "authors": [
    "Jaeyoung Lee",
    "Aravind Balakrishnan",
    "Ashish Gaurav",
    "Krzysztof Czarnecki",
    "Sean Sedwards"
  ],
  "categories": [
    "cs.LG",
    "cs.NE",
    "cs.PF",
    "stat.ML"
  ],
  "abstract": "Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have attempted to address this issue, but there are few publicly-available tools to adequately explore the trade-offs between functionality, scalability, and safety. We thus present WiseMove, a software framework to investigate safe deep reinforcement learning in the context of motion planning for autonomous driving. WiseMove adopts a modular learning architecture that suits our current research questions and can be adapted to new technologies and new questions. We present the details of WiseMove, demonstrate its use on a common traffic scenario, and describe how we use it in our ongoing safe learning research.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-02-11T19:59:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "safe deep reinforcement learning",
      "autonomous driving",
      "current research questions",
      "common traffic scenario",
      "safety remains"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}