Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Online Robustness Training for Deep Reinforcement Learning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1911.00887.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:03:15.071223Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T15:00:57.730134Z
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 93235302-0c37-49af-8fb0-29ccd104eaa9 · inbound
Towards Robust Deep Reinforcement Learning against Environmental State Perturbation cites this paper.
Towards Robust Deep Reinforcement Learning against Environmental State Perturbation Online Robustness Training for Deep Reinforcement Learning
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:03:15.071223Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d63bf7e-49bc-4d6c-b52e-e2b52d525f58 · inbound
Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimization cites this paper.
Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimization Online Robustness Training for Deep Reinforcement Learning
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:25:07.041555Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ccd6462-8518-4031-bcf8-dcc6cad74966 · inbound
Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach cites this paper.
Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach Online Robustness Training for Deep Reinforcement Learning
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:00:57.737034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.