{
  "apiVersion": "v1",
  "methodology": "https://sourcescore.org/methodology/",
  "canonical": "https://sourcescore.org/claims/8927d20ad0b3849f/",
  "claim": {
    "vertical": "ai-ml",
    "subject": "Deep Q-Network (DQN)",
    "predicate": "introduced_in_paper",
    "object": "Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013)",
    "confidence": 0.82,
    "sources": [
      {
        "url": "https://arxiv.org/abs/1312.5602",
        "title": "Playing Atari with Deep Reinforcement Learning",
        "publisher": "arXiv (Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al.)",
        "publishedDate": "2013-12-19",
        "accessedDate": "2026-06-01",
        "type": "preprint",
        "excerpt": "We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards."
      },
      {
        "url": "https://huggingface.co/papers/1312.5602",
        "title": "Playing Atari with Deep Reinforcement Learning (Hugging Face Papers)",
        "publisher": "Hugging Face",
        "accessedDate": "2026-06-01",
        "type": "docs"
      }
    ],
    "publishedAt": "2026-06-01T00:00:00Z",
    "lastVerified": "2026-06-01",
    "methodologyVersion": "veritas-v0.1",
    "tags": [
      "dqn",
      "deep-reinforcement-learning",
      "q-learning",
      "atari",
      "mnih",
      "deepmind",
      "foundational",
      "2013"
    ],
    "id": "8927d20ad0b3849f",
    "statement": "Deep Q-Network (DQN) introduced in paper: Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013)."
  },
  "signature": {
    "algorithm": "HMAC-SHA256",
    "signedBy": "did:web:sourcescore.org",
    "signedAt": "2026-06-02T00:00:00.000Z",
    "signature": "5ea76d072969edf30974fcbed8557a172ffe267603351dbd079b4b56870f0483"
  },
  "citedAs": "Deep Q-Network (DQN) introduced in paper: Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013). — SourceScore Claim 8927d20ad0b3849f (verified 2026-06-01, signed 5ea76d07…). https://sourcescore.org/claims/8927d20ad0b3849f/"
}