Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
JointPPO: Diving Deeper into the Effectiveness of PPO in Multi-Agent Reinforcement Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.11831.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:31:02.610322Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T18:41:12.396409Z
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 f8e47238-dac4-44e7-8eba-39a9b7f58bd4 · inbound
Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach cites this paper.
Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach JointPPO: Diving Deeper into the Effectiveness of PPO in Multi-Agent Reinforcement Learning
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:31:02.610322Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 046c970d-3db6-40bb-9868-c48042246b7b · inbound
Real-time adaptive quantum error correction by model-free multi-agent learning cites this paper.
Real-time adaptive quantum error correction by model-free multi-agent learning JointPPO: Diving Deeper into the Effectiveness of PPO in Multi-Agent Reinforcement Learning
Reference 111
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:37:10.410605Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5909f34-2ade-46be-973c-0048671faf96 · inbound
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters cites this paper.
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters JointPPO: Diving Deeper into the Effectiveness of PPO in Multi-Agent Reinforcement Learning
Reference 277
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:41:12.399712Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.