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Paper Citation Record · LEDGER

Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1812.00922.

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

pith.paper-citation-record.v1
1812.00922 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:22:09.628959Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T05:22:09.820215Z

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 bc86a9c6-c263-4a78-9d7f-06f5cf8d9b8c · inbound

Towards Fault Tolerance in Multi-Agent Reinforcement Learning cites this paper.

Towards Fault Tolerance in Multi-Agent Reinforcement Learning Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-12T05:22:09.827043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-12T05:22:09.628959Z digest=sha256:4d1c0ab79ee62336fc9edee933766d740af523734afa56e51c66d5df8057b365

Observation d9a7e3d6-236c-4c32-806b-e23b45ff8226 · inbound

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning cites this paper.

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations

Reference 56

Resolution
unresolved
no resolver link, observed 2026-07-13T03:08:01.590659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T03:08:01.590659Z digest=sha256:294f6f290c96f9f26950286e125d5cda535a133c2db03ef1ad869b9367117dd2