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Paper Citation Record · LEDGER

Empirical Design in Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.01315.

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

pith.paper-citation-record.v1
2304.01315 v2

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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:34:53.949455Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T03:09:43.582400Z

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 4669983a-3fea-4362-8fcd-17d4fc19d61b · inbound

A Survey of Reinforcement Learning For Economics cites this paper.

A Survey of Reinforcement Learning For Economics Empirical Design in Reinforcement Learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-02T18:34:53.949455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T18:34:53.949455Z digest=sha256:30dbecee24e8229e485492bbc986a642f617aec831e3299db3dcfb8b5e9ea70a

Observation 18c32a00-b6da-4feb-910b-ae870d2d00ab · inbound

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling cites this paper.

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling Empirical Design in Reinforcement Learning

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-05-15T03:09:43.587801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-05-15T03:05:36.871497Z digest=sha256:b7b70759d4a3a78812e53a89bda86011bfcd1eab6e9af364e67da199cbbff876