Paper Citation Record · LEDGER

State-wise Safe Reinforcement Learning: A Survey

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2302.03122.

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

pith.paper-citation-record.v1
2302.03122 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:00:05.724349Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T09:24:32.068752Z

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 350272cd-c349-4cbb-a677-6fefca8699a8 · inbound

Action Mapping for Reinforcement Learning in Continuous Environments with Constraints cites this paper.

Action Mapping for Reinforcement Learning in Continuous Environments with Constraints State-wise Safe Reinforcement Learning: A Survey

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T21:39:11.999964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54538de1-aa23-4c20-b6c2-5bd8e2cfe1de · inbound

Leveraging Constraint Violation Signals For Action-Constrained Reinforcement Learning cites this paper.

Leveraging Constraint Violation Signals For Action-Constrained Reinforcement Learning State-wise Safe Reinforcement Learning: A Survey

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T19:00:00.440808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79e8a757-6df0-4b3d-b3c7-3dc1547e5468 · inbound

Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning cites this paper.

Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning State-wise Safe Reinforcement Learning: A Survey

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:00:05.724349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaefb24a-b4ec-42bd-9bc9-8dbcf3644343 · inbound

Combee: Scaling Prompt Learning for Self-Improving Language Model Agents cites this paper.

Combee: Scaling Prompt Learning for Self-Improving Language Model Agents State-wise Safe Reinforcement Learning: A Survey

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T10:33:19.825936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd5d61c3-a6c1-4275-a68b-2cb198109785 · inbound

Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility cites this paper.

Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility State-wise Safe Reinforcement Learning: A Survey

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:22:37.422246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f4b85175-ceaa-4575-ae0f-36d31971f233 · inbound

Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization cites this paper.

Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization State-wise Safe Reinforcement Learning: A Survey

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.070573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.