Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1712.06560.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T12:32:58.773221Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T14:48:25.366889Z
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 605ac114-c863-47d4-97fe-679d0f20bd2e · inbound
Learning Representations and Agents for Information Retrieval cites this paper.
Learning Representations and Agents for Information Retrieval Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:00:57.741907Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d926de47-8351-47a2-a0a1-81169bf79e48 · inbound
What if Eye...? Computationally Recreating Vision Evolution cites this paper.
What if Eye...? Computationally Recreating Vision Evolution Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Reference 49
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-10T14:48:25.404243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1ea9a75e-2d0e-4a74-887f-317202e05aaa · inbound
Evolutionary Policy Optimization cites this paper.
Evolutionary Policy Optimization Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T12:32:58.773221Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.