Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1901.00210.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:54:13.784020Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-14T04:54:13.999483Z
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 7541a362-e33a-4c63-b5bc-e4617e947fd7 · inbound
$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank cites this paper.
$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T04:54:14.003851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 917d3c25-81d3-43ce-b267-189440240415 · inbound
Multi-agent imitation learning with function approximation: Linear Markov games and beyond cites this paper.
Multi-agent imitation learning with function approximation: Linear Markov games and beyond Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T20:42:07.973187Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ee2d9232-b4ba-489f-8d9f-c54ebef1d4d5 · inbound
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification cites this paper.
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T09:55:57.097421Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.