Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2007.13544.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T18:56:45.271049Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T20:58:57.264863Z
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 ae84acbf-5c05-42f1-97c3-5d9cec001d66 · inbound
Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder cites this paper.
Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T18:56:45.271049Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e8c78199-6de8-44ff-82dd-4034fd956a69 · inbound
How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions cites this paper.
How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T07:04:20.853307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 00c6dfa2-744a-4d0c-a421-93f7705c925f · inbound
Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games cites this paper.
Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T20:58:57.266451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.