Pith. sign in

Paper Citation Record · LEDGER

Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1709.03969.

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

pith.paper-citation-record.v1
1709.03969 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:30:29.318381Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T18:24:48.386780Z

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 37df2c17-0522-46f2-a4d4-3cbf9e01fe6c · inbound

Why Build an Assistant in Minecraft? cites this paper.

Why Build an Assistant in Minecraft? Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-24T18:24:48.389793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-24T18:20:58.081505Z digest=sha256:eb1cbc7b26fb419ff7fa36c1e384b1300e933332cbba53f9c272fbda9f4b30ba

Observation 489a2b68-8b34-4570-98b6-7dcc973c62d5 · inbound

Improving Deep Reinforcement Learning in Minecraft with Action Advice cites this paper.

Improving Deep Reinforcement Learning in Minecraft with Action Advice Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-14T15:30:29.318381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T15:30:29.318381Z digest=sha256:af1acb2adf4590a3d4eeeb1ed3ad13109bffd1f1e55611fea9dd57408ed634f3

Observation cf7e173d-bc97-4ad6-ba4b-caeb82844158 · inbound

Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework cites this paper.

Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds

Reference 112

Resolution
unresolved
no resolver link, observed 2026-08-12T18:15:15.720677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:15:15.720677Z digest=sha256:9f781f8926503ad19ebedd858a82e56450493f8114da90f7c665e5dd674f4c6b