Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Loss of Plasticity in Continual Deep Reinforcement Learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2303.07507.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:48:44.616239Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T07:55:58.968776Z
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 a3d7ccb0-92bc-4646-b308-e5a76539886e · inbound
How Should We Meta-Learn Reinforcement Learning Algorithms? cites this paper.
How Should We Meta-Learn Reinforcement Learning Algorithms? Loss of Plasticity in Continual Deep Reinforcement Learning
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:48:44.616239Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 11d7a36d-c0bd-4f1e-b91d-e3416e7bea1f · inbound
Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks cites this paper.
Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks Loss of Plasticity in Continual Deep Reinforcement Learning
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T07:55:58.971509Z
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.
Observation 66543f10-4b88-4ecd-adfc-a95f492d1793 · inbound
Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform cites this paper.
Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform Loss of Plasticity in Continual Deep Reinforcement Learning
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T18:18:00.317599Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.