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Paper Citation Record · LEDGER

Corruption-robust exploration in episodic reinforcement learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1911.08689.

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

pith.paper-citation-record.v1
1911.08689 v4

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:58:12.183239Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:58:14.743958Z

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 7a86f304-8b4b-49c8-b2ff-0ba0007b5722 · inbound

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning cites this paper.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Corruption-robust exploration in episodic reinforcement learning

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:58:14.820244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.183239Z digest=sha256:8bdc00f301ad16e5905b82db8e6d088fd46ade2208fc9c58870e0371f88e34a3

Observation 29c0e365-0bf4-4273-9e28-0bcf44422b12 · inbound

Online Learning in MDPs with Partially Adversarial Transitions and Losses cites this paper.

Online Learning in MDPs with Partially Adversarial Transitions and Losses Corruption-robust exploration in episodic reinforcement learning

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-03T03:01:39.214140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:01:39.214140Z digest=sha256:396d24c3999298438590ceaba805abba376fd59fe4b9dcc7e6410329052b7d15