Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Universal Successor Representations for Transfer Reinforcement Learning
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1804.03758.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:51:25.769961Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-15T15:35:10.782793Z
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 609da4df-8f92-4ecb-a7a7-742fb1610f90 · inbound
VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation cites this paper.
VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation Universal Successor Representations for Transfer Reinforcement Learning
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:51:25.769961Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 62b1e3bf-45fc-442d-a58d-6ef81d335aff · inbound
Learning Action-Transferable Policy with Action Embedding cites this paper.
Learning Action-Transferable Policy with Action Embedding Universal Successor Representations for Transfer Reinforcement Learning
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T04:59:31.367916Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7fb4b8a4-3f02-4d4c-ad20-d37ce9983452 · inbound
Is Conditional Generative Modeling all you need for Decision-Making? cites this paper.
Is Conditional Generative Modeling all you need for Decision-Making? Universal Successor Representations for Transfer Reinforcement Learning
Reference 165
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-15T15:35:10.787253Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.