Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Safe and Robust Reinforcement Learning: Principles and Practice
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.18539.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T16:02:26.039476Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T15:14:46.900287Z
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 7e2a01a0-c62d-43ac-9502-888055c068de · inbound
Active Query Selection for Crowd-Based Reinforcement Learning cites this paper.
Active Query Selection for Crowd-Based Reinforcement Learning Safe and Robust Reinforcement Learning: Principles and Practice
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:02:26.039476Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4c2a74bc-163c-49c2-9a55-a1d03bca2b20 · inbound
Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs cites this paper.
Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs Safe and Robust Reinforcement Learning: Principles and Practice
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T15:14:46.901529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b7711c85-4e2e-4d98-ab6e-c37882641af2 · inbound
A Distributionally Robust Multi-agent Reinforcement Learning Framework for Intelligent Intersection Control cites this paper.
A Distributionally Robust Multi-agent Reinforcement Learning Framework for Intelligent Intersection Control Safe and Robust Reinforcement Learning: Principles and Practice
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T14:44:28.797978Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.