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Paper Citation Record · LEDGER

Tianshou: a Highly Modularized Deep Reinforcement Learning Library

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.14171.

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

pith.paper-citation-record.v1
2107.14171 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:22:19.562842Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:29:49.522868Z

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 22b1d74f-e0e4-4154-9188-acb09ad8e412 · inbound

Gymnasium: A Standard Interface for Reinforcement Learning Environments cites this paper.

Gymnasium: A Standard Interface for Reinforcement Learning Environments Tianshou: a Highly Modularized Deep Reinforcement Learning Library

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:29:49.526610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T17:29:49.186565Z digest=sha256:12726494c5cd12ed7603ca1ea69b61dccaa7ee4c0c425957c877d2a558106901

Observation f21f248e-f555-4e3d-a345-a0a431a0a37a · inbound

When Maximum Entropy Misleads Policy Optimization cites this paper.

When Maximum Entropy Misleads Policy Optimization Tianshou: a Highly Modularized Deep Reinforcement Learning Library

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T10:22:19.562842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:19.562842Z digest=sha256:14a2c587b0559c27e9a8fd2725f21532eacd49e17c56be3978a3a10efeacb73d