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Paper Citation Record · LEDGER

A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2111.09794.

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

pith.paper-citation-record.v1
2111.09794 v6

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 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:45:58.928009Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, 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

13
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c9beded9-ef7a-432b-a013-f52a0be687d6 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Reference 180

Resolution
verified exact
arxiv_id, observed 2026-05-16T08:12:31.683851Z

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.

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:e164619334ebf557c99ef75da4036f288049b78b2983c81121b91d2237ffdd4e

Observation 121605d9-f04c-4902-b43f-7415e9ee3f99 · inbound

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing cites this paper.

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T15:45:58.928009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:45:58.928009Z digest=sha256:0fdd58cf00445cb2959ff13adbbdb05780a94fd0d493bed9ce6a6eeb86e239ec

Observation db90c9a6-09fb-4da6-994e-1a0b0fbe51fd · inbound

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving cites this paper.

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-15T04:45:01.025332Z

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.

source=pdf_text observed=2026-05-15T04:41:07.402168Z digest=sha256:9168273b252015a1c829f0e70d8bb912a180e66c84d4fe1b09a37471d78f7a79

Observation 9d640e33-c2f0-487d-b0c6-f37670f2ff4a · inbound

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving cites this paper.

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-21T07:49:49.999354Z

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.

source=pdf_text observed=2026-05-21T07:48:52.168457Z digest=sha256:3e7df677b45eac577201d7c3de4d606753e15c03faabd9e28bc15d6e4837d58a

Observation b861e198-4f2b-4245-b480-f2876ef367f5 · inbound

Reinforcement Learning from Cross-domain Videos with Video Prediction Model cites this paper.

Reinforcement Learning from Cross-domain Videos with Video Prediction Model A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Reference 21

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T02:26:26.929595Z

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.

source=pdf_text observed=2026-06-28T10:54:49.582908Z digest=sha256:32582b7041e1ffd58becd9a596ba4dd00c193ff0232cbb222d90323552c34893

Observation dc887029-78e1-4573-973a-bd8815cbbab2 · inbound

Reinforcement Learning: From Algorithms To Foundation Models cites this paper.

Reinforcement Learning: From Algorithms To Foundation Models A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Reference 141

Resolution
unresolved
no resolver link, observed 2026-08-01T17:45:10.592865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:45:10.592865Z digest=sha256:665808643c87ea622bf52ea72dccd731b9c3c61a51ff03198856dd592d1b33b3