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Paper Citation Record · LEDGER

Approximate information state based convergence analysis of recurrent Q-learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.05991.

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

pith.paper-citation-record.v1
2306.05991 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:24:39.272202Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T05:41:23.524551Z

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 11dea12b-954f-49e2-92a9-b37acfda843d · inbound

Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning cites this paper.

Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning Approximate information state based convergence analysis of recurrent Q-learning

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-11T19:24:39.272202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:24:39.272202Z digest=sha256:4163a9dfcdc670650bd286f6e1956e03452875426e098a63ead52e8646729f51

Observation 9bbdbc8e-9d32-4e90-8ddd-399136549782 · inbound

Policy Gradient Methods for Non-Markovian Reinforcement Learning cites this paper.

Policy Gradient Methods for Non-Markovian Reinforcement Learning Approximate information state based convergence analysis of recurrent Q-learning

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:41:23.528303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-12T05:36:54.894355Z digest=sha256:a094d9c4f0f852eb4e6dcc59b7f43096f78e6f37104a3a72925f4fb1bb8d9954