Paper Citation Record · LEDGER

Estimating Risk and Uncertainty in Deep Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1905.09638.

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

pith.paper-citation-record.v1
1905.09638 v5

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:59:06.596214Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:06:44.599011Z

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 3d61215e-54bc-4d8f-b29d-5e3dbb6dcb63 · inbound

Uncertainty Prioritized Experience Replay cites this paper.

Uncertainty Prioritized Experience Replay Estimating Risk and Uncertainty in Deep Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:59:06.596214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ee5912d-1e33-4e7c-8b00-ce961763df79 · inbound

MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation cites this paper.

MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation Estimating Risk and Uncertainty in Deep Reinforcement Learning

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T07:17:30.663372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 360b2de1-ecbf-4057-b859-96c7a0e4d621 · inbound

RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach cites this paper.

RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach Estimating Risk and Uncertainty in Deep Reinforcement Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-21T09:54:58.223600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f9136812-f7b2-435d-bc13-90d8073d476d · inbound

Neural Computers cites this paper.

Neural Computers Estimating Risk and Uncertainty in Deep Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:20:46.840802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f114e5c5-7612-4dee-bd08-df3ee27268db · inbound

Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees cites this paper.

Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees Estimating Risk and Uncertainty in Deep Reinforcement Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:06:44.600423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81d0e5b2-0f0c-4829-9f45-d5455a0fa4ef · inbound

Auditing the Risk Claims of Distributional Reinforcement Learning cites this paper.

Auditing the Risk Claims of Distributional Reinforcement Learning Estimating Risk and Uncertainty in Deep Reinforcement Learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-07-14T04:26:46.686445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.