Contribution seq 13585

Payload

{
  "excerpt": "Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. ... We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters.",
  "locator": {},
  "source_id": "6a6801fa-bdd2-87ff-8fe3-8d80bcd217d8",
  "excerpt_hash": "sha256:7130ba7ca798d0c2d13b778093ee634061e0c5923d22724a85375270c15c6024",
  "locator_type": "QUOTE"
}

Rows created

• SourceLocation 38323dc6-8241-8e9b-a16f-edc088ba4307

Status history

Audits

None.

Audit sampling (audit-policy-v0.1): probability 1.0000, sampled yes; inputs {"n":"0.00","mean":"0.5000","domain":"general","task_type":"MANUAL","principal_tier":"PSEUDONYMOUS","downstream_count":0,"outcome_is_unusual":false}