Paper Citation Record · LEDGER

A Theory of Abstraction in Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2203.00397.

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

pith.paper-citation-record.v1
2203.00397 v1

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 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:10:40.440922Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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 62eff5e4-c4ae-4840-b3da-ab2cd23329e9 · inbound

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications cites this paper.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications A Theory of Abstraction in Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:40.440922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc0284af-a214-4ae6-bd9a-17278f658440 · inbound

A Survey of State Representation Learning for Deep Reinforcement Learning cites this paper.

A Survey of State Representation Learning for Deep Reinforcement Learning A Theory of Abstraction in Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:34:30.715934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ef74acd-2049-400b-8cae-8d86af2074c3 · inbound

Abstract Sim2Real through Approximate Information States cites this paper.

Abstract Sim2Real through Approximate Information States A Theory of Abstraction in Reinforcement Learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-10T10:44:38.109266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e5e2c5bd-969e-4bf1-b31f-1775f4316913 · inbound

Bayesian updates from coalgebraic determinisation cites this paper.

Bayesian updates from coalgebraic determinisation A Theory of Abstraction in Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:27:23.844352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.