Paper Citation Record · LEDGER

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers

As of 21 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 2 inbound Pith citation observations for arXiv:2411.15370.

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

pith.paper-citation-record.v1
2411.15370 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:28:36.463613Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:16:29.076338Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:23:22.088073Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy16
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ae6fafe-f848-4081-b498-fde9a086bc11 · outbound

This paper cites an unresolved cited work.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:28:37.766178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fd06cfa7-acb9-42d5-95ec-84cbe33549f5 · outbound

This paper cites an unresolved cited work.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:28:37.755738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aa13e95c-d80e-479c-9b5b-07d3548afc0f · outbound

This paper cites If ˆY is an unbiased estimator of ¯Y and { ˆYj}j are i.i.d.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers If ˆY is an unbiased estimator of ¯Y and { ˆYj}j are i.i.d

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.664235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5ee2c12d-34a0-4015-8a43-4125c5648cc3 · outbound

This paper cites If ˆy is an unbiased estimator of ¯y and {yj}j are i.i.d.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers If ˆy is an unbiased estimator of ¯y and {yj}j are i.i.d

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.557407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 01d77d02-9cb7-4394-862f-b8e99688825f · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.546652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4eb8622f-e1df-4424-95cc-a3f8043b31da · outbound

This paper cites Limitations.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Limitations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.424635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4c169bb7-0ce9-404a-965f-bdaf10f68f15 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.317713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43353a88-73fa-42b4-b7cd-100aff96d0fc · outbound

This paper cites We provide pseudo- code and implementation details which are easy to follow and reproduce.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers We provide pseudo- code and implementation details which are easy to follow and reproduce

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.307848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9701a506-9e26-4dcc-846e-5aef48b1adc7 · outbound

This paper cites All data can be generated during training.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers All data can be generated during training

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.198589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aabcf87c-1da2-4887-be5a-a45e22a20e85 · outbound

This paper cites We also list important hyper-parameters, neural network architectures, and other training details in the appendix.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers We also list important hyper-parameters, neural network architectures, and other training details in the appendix

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.045620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7bb0f572-ce8a-49cc-8adf-b4389de3351f · outbound

This paper cites All results are averaged over 30 runs and reported with 95% confidence interval.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers All results are averaged over 30 runs and reported with 95% confidence interval

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.035305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b26a0333-19a7-444f-904e-9258f11ec5ff · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Guidelines: • The answer NA means that the paper does not include experiments

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.024001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7b98a8d1-a70b-4352-b53a-97f9a4d3dea4 · outbound

This paper cites • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:37.013746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 598d7125-4740-43fe-868a-b06a68f200dd · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:36.759056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 62fa513f-08b8-4688-a0d1-7da0336bc486 · outbound

This paper cites an unresolved cited work.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:28:36.748818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3da4fab0-88f2-450e-931d-1eae4bd66df1 · outbound

This paper cites All our results are generated during training.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers All our results are generated during training

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:36.738384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a2515985-b436-4cf4-91ef-c5c390052cce · outbound

This paper cites • Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers • Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:36.728013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c11a8980-e900-4a47-a2d6-18fdee074516 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:36.716860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 095d6426-efb0-4455-8c3e-f0f3bbc49eba · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:28:36.503641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 5f805f2b-8114-45fc-9733-87f9dd8099b4 · inbound

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

The Hive Mind is a Single Reinforcement Learning Agent Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:23:22.090290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd65b16c-a140-415e-b947-da1f44889529 · inbound

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning cites this paper.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T19:16:29.076338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.