Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2101.11071.
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 2026-08-07T13:30:21.691053Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T15:46:53.593659Z
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 b1007fcf-d787-48f9-9dfb-49bb58bc0f83 · 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 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:11:41.201596Z
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 ebe1343e-c15c-4712-b88c-2e23bef46a6f · inbound
Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation cites this paper.
Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:30:21.691053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed3defb1-0579-403f-8355-5c3e25031206 · inbound
Forager: a lightweight testbed for continual learning with partial observability in RL cites this paper.
Forager: a lightweight testbed for continual learning with partial observability in RL The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:46:53.667983Z
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.