Pith. sign in

Paper Citation Record · LEDGER

The Challenges of Exploration for Offline Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2201.11861.

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

pith.paper-citation-record.v1
2201.11861 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:14:53.673901Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T03:08:59.576204Z

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 eb952f6b-4f00-45dd-85b8-f8fecc5cd21a · inbound

Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search cites this paper.

Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search The Challenges of Exploration for Offline Reinforcement Learning

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T20:47:41.274317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:47:41.274317Z digest=sha256:4a45dd09afa476a73381f1f0e8736d2cc5b162efd98c389b792483016d383b2f

Observation 8a03cccb-ef8c-4ab1-b27c-4eed5d63a55d · inbound

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity cites this paper.

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity The Challenges of Exploration for Offline Reinforcement Learning

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-15T19:14:53.673901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:14:53.673901Z digest=sha256:89cce46daca65ae2b95d7896392359861b1aef50be5895dd1a15508a863020e7

Observation d3d698d3-d6f9-4d7c-82aa-0348fb02cc16 · inbound

Unsupervised Data Generation for Offline Reinforcement Learning: A Perspective from Model cites this paper.

Unsupervised Data Generation for Offline Reinforcement Learning: A Perspective from Model The Challenges of Exploration for Offline Reinforcement Learning

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-15T18:36:09.983800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:36:09.983800Z digest=sha256:90315f1595bbec09201dbf333c90429570a99ca145bd86604c0fbb689e1b50ab

Observation 29d57b13-7a25-4cb6-96c9-1feb36e55bda · inbound

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling cites this paper.

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling The Challenges of Exploration for Offline Reinforcement Learning

Reference 126

Resolution
verified exact
arxiv_id, observed 2026-05-15T03:08:59.579203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-15T03:05:36.871497Z digest=sha256:287e62cf4d22381cc4bde1b81a530c8666b3e1601db6167ce9fcf101f3bf943b