Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T11:09:53.140241Z
Paper Citation Record · LEDGER
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning
As of 11 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2501.16729.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
13 of 13 outbound references displayed
- verified exact3
- verified fuzzy3
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation aa3df037-c852-4563-80a9-93dae079605e · outbound
This paper cites On the Convergence of Bounded Agents.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning On the Convergence of Bounded Agents
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T11:09:53.080811Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0551a782-3f3f-45d0-9f26-36c02e5023b8 · outbound
This paper cites Discovering sensor space: Constructing spatial embeddings that explain sensor correlations.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Discovering sensor space: Constructing spatial embeddings that explain sensor correlations
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T11:09:53.335451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4603d88a-8037-4bca-bb0b-33dd4403e661 · outbound
This paper cites The Quest for a Common Model of the Intelligent Decision Maker.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning The Quest for a Common Model of the Intelligent Decision Maker
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T11:09:53.124567Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e0f6b91-5f13-4444-9c1a-67e6718d856c · outbound
This paper cites Are Sparse Neural Networks Better Hard Sample Learners?.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Are Sparse Neural Networks Better Hard Sample Learners?
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T11:09:53.195372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ab5a8035-9cd6-4b54-826e-b94c4294184b · outbound
This paper cites friendly bullet.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning friendly bullet
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T11:09:53.322735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6ef124e3-d861-466a-bdd7-0813c8abde34 · outbound
This paper cites Learning Sparse Representations Incrementally in Deep Reinforcement Learning.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Learning Sparse Representations Incrementally in Deep Reinforcement Learning
Reference 2009
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T11:09:53.295584Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 00377648-d430-425a-9209-2b49a9a582b3 · outbound
This paper cites Towards model-free RL algorithms that scale well with unstructured data.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Towards model-free RL algorithms that scale well with unstructured data
Reference 2010
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T11:09:53.114264Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9dc372db-5ba6-44cf-b234-49a0e39bc74b · outbound
This paper cites Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
Reference 2015
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T11:09:53.100144Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0eba19ac-3188-4b91-8c79-d5841eec00d5 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Adam: A Method for Stochastic Optimization
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T11:09:53.095599Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c48158d8-2415-4c57-9eed-ee3a05d5165c · outbound
This paper cites Adapting the Function Approximation Architecture in Online Reinforcement Learning.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Adapting the Function Approximation Architecture in Online Reinforcement Learning
Reference 2019
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T11:09:53.253636Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6ddd49d8-d4f4-4845-b401-56ece39a90a9 · outbound
This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T11:09:53.119007Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73d87bf9-89ec-40aa-a033-1271719fcba0 · outbound
This paper cites Taylor, Mykola Pechenizkiy, and Decebal Constantin Mocanu.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Taylor, Mykola Pechenizkiy, and Decebal Constantin Mocanu
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T11:09:53.348186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7c0cae67-9116-4eaf-a8fd-4e9c07243c9e · outbound
This paper cites MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments.
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T11:09:53.135324Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.