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Paper Citation Record · LEDGER

Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1908.04683.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.04683 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:51:20.055459Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T01:27:32.016449Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3f058dab-c0e3-4fee-b72b-113b5c2a9a7f · inbound

Mastering Atari with Discrete World Models cites this paper.

Mastering Atari with Discrete World Models Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-15T01:27:32.018223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-15T01:27:31.813680Z digest=sha256:5bdc0f1923b43a03e9e6eba140916c460061dc30b61f049a0fda64ad570c8b56

Observation 41ed2bce-0f88-4081-99f2-d5d7b7676aee · inbound

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi cites this paper.

Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T19:51:20.055459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:51:20.055459Z digest=sha256:afe4577bf5a6919048a2fbd78ae2c1a820e6fb9fdd0048e74d75cd6921534187

Observation f2a56067-4a5d-4bab-9a84-17caabb8e52f · inbound

Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning cites this paper.

Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-09T21:16:56.496069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:16:56.496069Z digest=sha256:fa684bd94ffe7e81f04ca7ccc8e4177ce9c073b5cd6a83ae6a11cb358a8776c7

Observation 91af73b3-8df4-4f02-aa44-4848b0c413a5 · inbound

Playstyle and Artificial Intelligence: An Initial Blueprint Through the Lens of Video Games cites this paper.

Playstyle and Artificial Intelligence: An Initial Blueprint Through the Lens of Video Games Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field

Reference 295

Resolution
unresolved
no resolver link, observed 2026-08-05T16:00:11.585426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:00:11.585426Z digest=sha256:beb1a0585556bf95bcd4de20d9748494771390f1771ced6d88d3de99a38a56a0