Paper Citation Record · LEDGER

Streaming Deep Reinforcement Learning Finally Works

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2410.14606.

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

pith.paper-citation-record.v1
2410.14606 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:56:58.240625Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9aa87186-d278-4cb2-9698-e7d2da420e9c · inbound

The Hive Mind is a Single Reinforcement Learning Agent cites this paper.

The Hive Mind is a Single Reinforcement Learning Agent Streaming Deep Reinforcement Learning Finally Works

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:23:22.085549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9091e482-7ab6-4f0d-be5c-ea4d9fc5c357 · inbound

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams cites this paper.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Streaming Deep Reinforcement Learning Finally Works

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T18:56:58.240625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0243adc6-cfa2-4e29-b300-26f742d32192 · inbound

Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation cites this paper.

Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation Streaming Deep Reinforcement Learning Finally Works

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:05:58.029842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c61165e3-a60e-4d8f-9f39-f86c2367a983 · inbound

Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation cites this paper.

Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigation Streaming Deep Reinforcement Learning Finally Works

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T00:08:53.253285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dac0081-6aa4-4ae7-be1e-9258c06fa597 · inbound

Intentional Updates for Streaming Reinforcement Learning cites this paper.

Intentional Updates for Streaming Reinforcement Learning Streaming Deep Reinforcement Learning Finally Works

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:21:06.688003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86c0b96a-61c0-4500-ac5e-42cf2279d343 · inbound

Extending Differential Temporal Difference Methods for Episodic Problems cites this paper.

Extending Differential Temporal Difference Methods for Episodic Problems Streaming Deep Reinforcement Learning Finally Works

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:35:38.877406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3880f559-340b-4b82-b027-d574fb1009d6 · inbound

Revisiting Adam for Streaming Reinforcement Learning cites this paper.

Revisiting Adam for Streaming Reinforcement Learning Streaming Deep Reinforcement Learning Finally Works

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T01:15:51.976420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ed063e17-cabf-4ed5-8e14-128be6ec807f · inbound

Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning cites this paper.

Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning Streaming Deep Reinforcement Learning Finally Works

Reference 124

Resolution: unresolved; no resolver link, observed 2026-07-31T00:47:51.455172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.