Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1904.10079.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T17:02:00.653422Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:47:30.355469Z
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 ff45873c-200e-473e-9d2f-9358eb41580b · inbound
Optimal Use of Experience in First Person Shooter Environments cites this paper.
Optimal Use of Experience in First Person Shooter Environments The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T17:46:05.954369Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation faf16168-e875-43f8-a38b-942d51023405 · inbound
CraftAssist: A Framework for Dialogue-enabled Interactive Agents cites this paper.
CraftAssist: A Framework for Dialogue-enabled Interactive Agents The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-24T19:09:50.193079Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f6c096fe-0d8b-41a9-9b92-8eee280d3a34 · inbound
Dota 2 with Large Scale Deep Reinforcement Learning cites this paper.
Dota 2 with Large Scale Deep Reinforcement Learning The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T22:18:15.733200Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4a45f81b-f56d-405a-a9c6-5867a85a823e · inbound
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents cites this paper.
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T03:27:40.715704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ada4b94d-6eac-4a46-93e4-6dfacbdb7b50 · inbound
Voyager: An Open-Ended Embodied Agent with Large Language Models cites this paper.
Voyager: An Open-Ended Embodied Agent with Large Language Models The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 62
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:11:41.199212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation adce8110-474e-48a9-92be-15b2d874f841 · inbound
MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation cites this paper.
MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:47:30.357339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.