Paper Citation Record · LEDGER

Learning to Walk via Deep Reinforcement Learning

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1812.11103.

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

pith.paper-citation-record.v1
1812.11103 v3

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 2026-08-05T06:32:48.257954+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:39:32.224984Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T03:59:33.473334Z

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 5344e850-54f4-431e-8aac-6bef68d897ad · inbound

Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation cites this paper.

Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation Learning to Walk via Deep Reinforcement Learning

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-15T09:49:54.532049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dcdab800-9235-477a-b240-98a1c7b04adc · inbound

Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards cites this paper.

Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards Learning to Walk via Deep Reinforcement Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:38:15.084843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7eac299d-3f11-488a-93c3-8a3232e01ae1 · inbound

Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain cites this paper.

Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain Learning to Walk via Deep Reinforcement Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:16:29.215901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation be9cc1ac-1a21-4896-abf0-5eb1aca2a276 · inbound

Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain cites this paper.

Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain Learning to Walk via Deep Reinforcement Learning

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-01T13:45:45.925510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c503ae92-2b8d-4891-84ca-2cca180361d9 · inbound

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World cites this paper.

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World Learning to Walk via Deep Reinforcement Learning

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-04T03:59:33.475797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 17fc5320-4f6e-46f2-a632-63646128478c · inbound

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback cites this paper.

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback Learning to Walk via Deep Reinforcement Learning

Reference 278

Resolution: unresolved; no resolver link, observed 2026-08-03T04:39:32.224984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.