Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.04792.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-01T05:28:28.298787Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T10:25:42.279123Z
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 5d69c715-bb0a-45be-bed2-a36db91dc881 · inbound
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL cites this paper.
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:56:05.358455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ac0ef233-a1f2-4cba-84c8-9a2b3b4ae1c8 · inbound
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL cites this paper.
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T23:45:08.144479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 885ddcd0-b9ed-4882-9a81-0af74ef93f18 · inbound
FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion cites this paper.
FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
Reference 27
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T10:25:42.280503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.