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Paper Citation Record · LEDGER

Wasserstein Unsupervised Reinforcement Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.07940.

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

pith.paper-citation-record.v1
2110.07940 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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:47:05.670497Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T04:47:05.918689Z

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 857f1670-9d4e-432b-99e7-5be13481fc37 · inbound

Wasserstein Policy Optimization cites this paper.

Wasserstein Policy Optimization Wasserstein Unsupervised Reinforcement Learning

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-08-16T04:47:05.922391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-16T04:47:05.670497Z digest=sha256:c8c473e1eee16244c8b8797b5e455eb792c277660755d66215462c79b0cbf608

Observation 39d1511c-1f63-41a5-933a-69effd70f3a2 · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills Wasserstein Unsupervised Reinforcement Learning

Reference 88

Resolution
unresolved
no resolver link, observed 2026-08-04T19:45:32.998178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:45:32.998178Z digest=sha256:dccfe5fef1ef25bc5f00830ca8d5cc24edd6c7a91f379b9ba6e21c8011e8488d