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Paper Citation Record · LEDGER

Learnings Options End-to-End for Continuous Action Tasks

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1712.00004.

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

pith.paper-citation-record.v1
1712.00004 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T22:11:35.277901Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

34
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation efb24265-220d-40e9-bcd5-663f42c7cf31 · inbound

DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL cites this paper.

DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL Learnings Options End-to-End for Continuous Action Tasks

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-05-25T17:21:05.062024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-25T17:19:38.188254Z digest=sha256:a64caba4c23cbfaf74f4cbad51cdb2d4789629fdffedf40138dc35cb80e937fe

Observation 6cb883ba-2c7b-42ec-86ed-67f76c8a8ed0 · inbound

Scalable Option Learning in High-Throughput Environments cites this paper.

Scalable Option Learning in High-Throughput Environments Learnings Options End-to-End for Continuous Action Tasks

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-05-18T20:06:49.758336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-05-18T20:04:58.064472Z digest=sha256:98e4df656aaa06be6b9b7468d500d5b7df701186c13761f59dce819dc8d56344

Observation 96fc1a5b-e182-4b8c-b6ba-abd7214f2779 · inbound

Hierarchical Behaviour Spaces cites this paper.

Hierarchical Behaviour Spaces Learnings Options End-to-End for Continuous Action Tasks

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:01:12.814071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-08T03:33:48.527621Z digest=sha256:efffac5bafe58331b337a410d16ec2fb339947372a89d18cd9f1d60d5503cc7b

Observation ce45e8d6-6bf1-463c-9915-45efdf6e1743 · inbound

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning cites this paper.

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning Learnings Options End-to-End for Continuous Action Tasks

Reference 73

Resolution
verified exact
local_arxiv, observed 2026-07-01T14:15:46.628982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-06-30T22:11:35.277901Z digest=sha256:b098158b8203921642d0c7c100c192e83d29b9ca18d3fbab8ffb4f2187bd89e3

Observation 895e1e8b-936d-4ac0-a525-3f8bbbf561a9 · inbound

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning cites this paper.

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning Learnings Options End-to-End for Continuous Action Tasks

Reference 74

Resolution
verified exact
local_arxiv, observed 2026-06-30T22:15:05.965388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-06-30T22:11:35.277901Z digest=sha256:34e76fc06e01ebc9af51beef6b23842337547790db83149165cd106dda241b19

Observation af30ee5d-5ec9-4cc8-830d-afd0fa978b90 · inbound

Hierarchical Reinforcement Learning for Sparse-Reward Search in Commutative Algebra cites this paper.

Hierarchical Reinforcement Learning for Sparse-Reward Search in Commutative Algebra Learnings Options End-to-End for Continuous Action Tasks

Reference 9

Resolution
metadata mismatch
local_arxiv, observed 2026-06-26T09:19:15.963740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-06-26T09:16:18.520375Z digest=sha256:cca4ead79c8f816715a69e38cc0ff1cb6cd24bb116b48406464b30667c1aa83f