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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 2 inbound Pith citation observations for arXiv:1712.06560.

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

pith.paper-citation-record.v1
1712.06560 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:00:57.741907Z

measured 0 of 1 external citation measurements

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.

source=pdf_text observed=2026-08-14T13:00:57.741907Z digest=sha256:797b881ead2e03798d81a5a7007dcd701f487c08386bde4a0389762e6bcd546c

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T14:48:24.552811Z digest=sha256:11567050bffc559fb4e0450842c6755209625d29cb7726153c1b3e99b3fd43d7