Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1806.06877.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:24:03.264842Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T17:21:05.069954Z
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 ce40f3d7-c32a-4883-9be8-f0937e555e63 · 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 A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T17:21:05.073019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2f3b9682-b733-4ec5-9af4-d84850c738f8 · inbound
Generative AI for Autonomous Driving: A Review cites this paper.
Generative AI for Autonomous Driving: A Review A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:24:03.264842Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 97dffea0-2565-4375-bbcf-0238739a0749 · inbound
Is Optimal Transport Necessary for Inverse Reinforcement Learning? cites this paper.
Is Optimal Transport Necessary for Inverse Reinforcement Learning? A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:55:44.260548Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.