Verified claim · AI-ML · 82% confidence
Deep Q-Network (DQN) introduced in paper: Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013).
Last verified 2026-06-01 · Methodology veritas-v0.1 · 8927d20ad0b3849f
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Structured fields
- Subject
- Deep Q-Network (DQN)
- Predicate
introduced_in_paper- Object
- Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013)
- Confidence
- 82%
- Tags
- dqn · deep-reinforcement-learning · q-learning · atari · mnih · deepmind · foundational · 2013
Sources (2)
[1] preprint · arXiv (Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al.) · 2013-12-19
Playing Atari with Deep Reinforcement Learning“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.”
[2] docs · Hugging Face
Playing Atari with Deep Reinforcement Learning (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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The record lists 2 cited source(s): arXiv (Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al.), Hugging Face. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/8927d20ad0b3849f.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
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cURL
curl https://sourcescore.org/api/v1/claims/8927d20ad0b3849f.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/8927d20ad0b3849f.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Deep Q-Network (DQN) introduced in paper: Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/8927d20ad0b3849f.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Deep Q-Network (DQN) introduced in paper: Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_deep_q_network_dqn_fact() -> dict:
"""Fetch the verified SourceScore claim for Deep Q-Network (DQN)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/8927d20ad0b3849f.json")
return r.json()