Paper Citation Record · LEDGER

Mix&Match - Agent Curricula for Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1806.01780.

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

pith.paper-citation-record.v1
1806.01780 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:14:54.379501Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T13:25:52.393046Z

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 8cba6912-f8b4-415f-9083-ab27da064c89 · inbound

Growing Action Spaces cites this paper.

Growing Action Spaces Mix&Match - Agent Curricula for Reinforcement Learning

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-25T13:25:52.395718Z

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 45b4e543-c692-4af6-a0fb-47e9e42c4aa3 · inbound

Attentive Multi-Task Deep Reinforcement Learning cites this paper.

Attentive Multi-Task Deep Reinforcement Learning Mix&Match - Agent Curricula for Reinforcement Learning

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T02:25:14.643750Z

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 b86d6ea2-f478-4273-8d6a-87e940de59c2 · inbound

Dota 2 with Large Scale Deep Reinforcement Learning cites this paper.

Dota 2 with Large Scale Deep Reinforcement Learning Mix&Match - Agent Curricula for Reinforcement Learning

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-12T22:18:15.798083Z

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 6daf6545-cc68-4948-8d54-388426735fff · inbound

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance cites this paper.

Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance Mix&Match - Agent Curricula for Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T10:14:54.379501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2774e78-5124-49e0-b9fa-363c5e9f6d76 · inbound

Real-time adaptive quantum error correction by model-free multi-agent learning cites this paper.

Real-time adaptive quantum error correction by model-free multi-agent learning Mix&Match - Agent Curricula for Reinforcement Learning

Reference 108

Resolution: unresolved; no resolver link, observed 2026-08-05T10:37:10.403404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.