Paper Citation Record · LEDGER

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks

As of 23 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 2 inbound Pith citation observations for arXiv:2506.03404.

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

pith.paper-citation-record.v1
2506.03404 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:10:56.813881Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:39:04.763706Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T04:39:12.260604Z

Reference resolution

70 of 70 outbound references displayed

verified exact2
verified fuzzy44
unresolved24
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a7478523-5f34-4159-828e-329773f49616 · outbound

This paper cites Legged locomotion in challenging terrains using egocentric vision.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Legged locomotion in challenging terrains using egocentric vision

Reference 1

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Observation eca2519f-50f5-457a-b265-69a03635d151 · outbound

This paper cites S., Courville, A.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks S., Courville, A

Reference 2

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Observation 571ba2f3-5c9f-4f20-9e7d-abee91a456fa · outbound

This paper cites Atari-5: Distilling the arcade learning environment down to five games.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Atari-5: Distilling the arcade learning environment down to five games

Reference 3

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Observation 76acbe79-a8e9-4cab-bcc0-6246775d7094 · outbound

This paper cites What matters for on-policy deep actor-critic methods? a large-scale study.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks What matters for on-policy deep actor-critic methods? a large-scale study

Reference 4

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Observation 219395e5-b7f9-4581-9bba-c6f5fe5cdafb · outbound

This paper cites For valid generalization the size of the weights is more important than the size of the network.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks For valid generalization the size of the weights is more important than the size of the network

Reference 5

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Observation 82f633c1-36fa-4f3b-9675-83503e95af7c · outbound

This paper cites G., Naddaf, Y., Veness, J., and Bowling, M.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks G., Naddaf, Y., Veness, J., and Bowling, M

Reference 6

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Observation a5784aab-2c03-40b8-be08-c67910cd0629 · outbound

This paper cites G., Candido, S., Castro, P.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks G., Candido, S., Castro, P

Reference 7

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Observation d3718954-094b-45de-a396-ac95505cd461 · outbound

This paper cites A study on the plasticity of neural networks.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks A study on the plasticity of neural networks

Reference 8

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Observation bef80737-9e1b-48f6-bb76-b0eef9fbdee6 · outbound

This paper cites Exploration by random network distillation.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Exploration by random network distillation

Reference 9

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Observation b790cf31-f0a3-49fa-b27c-c0d0fb5d1e50 · outbound

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Unresolved cited work

Reference 10

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Observation bc5d8d8c-d2b8-43e5-ae2e-0ec11043beb1 · outbound

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Unresolved cited work

Reference 11

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Reference 12

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Observation b4ce3b45-129a-4503-a1d1-13fedd8621e3 · outbound

This paper cites Leveraging procedural generation to benchmark reinforcement learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Leveraging procedural generation to benchmark reinforcement learning

Reference 13

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Observation d0e80eec-7e75-4be7-b861-4e9fa25ef278 · outbound

This paper cites W., Hilton, J., Klimov, O., and Schulman, J.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks W., Hilton, J., Klimov, O., and Schulman, J

Reference 14

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Observation 52af0b67-690b-46a6-a601-0631541f6e2e · outbound

This paper cites G., and Courville, A.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks G., and Courville, A

Reference 15

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Observation 2a11f4f1-8013-438b-ad3d-3eb51ff26f66 · outbound

This paper cites Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures

Reference 16

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Observation dbffcd19-57e9-4cb5-b01d-a6fca9cd7eea · outbound

This paper cites J., Schrittwieser, J., Swirszcz, G., et al.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks J., Schrittwieser, J., Swirszcz, G., et al

Reference 17

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Observation f5e31804-8085-4696-9cd2-b998016c275e · outbound

This paper cites Simplifying Deep Temporal Difference Learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Simplifying Deep Temporal Difference Learning

Reference 18

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Observation bcd6fdd8-3c73-4499-8318-693b46e9710d · outbound

This paper cites On proximal policy optimization’s heavy-tailed gradients.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks On proximal policy optimization’s heavy-tailed gradients

Reference 19

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Observation 11584fc6-20df-49e0-af8d-2facb6f4701c · outbound

This paper cites Dextreme: Transfer of agile in-hand manipulation from simulation to reality.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Dextreme: Transfer of agile in-hand manipulation from simulation to reality

Reference 20

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Observation 5589d461-98e0-45f2-88dc-bcb933111d06 · outbound

This paper cites R., Millman, K.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks R., Millman, K

Reference 21

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Observation 40b2fb77-f918-43b9-ab7f-aadb9e97db1d · outbound

This paper cites Batch size-invariance for policy optimization.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Batch size-invariance for policy optimization

Reference 22

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This paper cites Improving neural networks by preventing co-adaptation of feature detectors.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Improving neural networks by preventing co-adaptation of feature detectors

Reference 23

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This paper cites Distributed prioritized experience replay.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Distributed prioritized experience replay

Reference 24

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks and Ash, J

Reference 27

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This paper cites Towards continual reinforcement learning: A review and perspectives.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Towards continual reinforcement learning: A review and perspectives

Reference 28

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Jupyter Notebooks a publishing format for reproducible computational workflows

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This paper cites Pgx: Hardware-accelerated parallel game simulators for reinforcement learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Pgx: Hardware-accelerated parallel game simulators for reinforcement learning

Reference 30

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks and Hertz, J

Reference 31

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Offline q-learning on diverse multi-task data both scales and generalizes

Reference 32

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This paper cites Parallel q-learning: Scaling off-policy reinforcement learning under massively parallel simulation.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Parallel q-learning: Scaling off-policy reinforcement learning under massively parallel simulation

Reference 33

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This paper cites Acceleration for deep reinforcement learning using parallel and distributed computing: A survey.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Acceleration for deep reinforcement learning using parallel and distributed computing: A survey

Reference 34

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks A., Pascanu, R., and Dabney, W

Reference 35

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This paper cites Normalization and effective learning rates in reinforcement learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Normalization and effective learning rates in reinforcement learning

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This paper cites Isaac gym: High performance gpu based physics simulation for robot learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Isaac gym: High performance gpu based physics simulation for robot learning

Reference 37

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This paper cites Miles 430 macklin, david hoeller, nikita rudin, arthur allshire, ankur handa, and gavriel state.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Miles 430 macklin, david hoeller, nikita rudin, arthur allshire, ankur handa, and gavriel state

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Effective sample size for importance sampling based on discrepancy measures

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Unresolved cited work

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Python for Data Analysis: Data Wrangling with Pandas, NumPy , and IPython

Reference 42

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks A., Veness, J., Bellemare, M

Reference 43

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K

Reference 44

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks No representation, no trust: Connecting representation, collapse, and trust issues in PPO

Reference 45

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This paper cites Norm-based capacity control in neural networks.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Norm-based capacity control in neural networks

Reference 46

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks The primacy bias in deep reinforcement learning

Reference 47

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Unresolved cited work

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Unresolved cited work

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This paper cites Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:59.307664Z

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Observation 032c4486-49c1-41d8-ad3b-42ebd99f1854 · outbound

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Unresolved cited work

Reference 51

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This paper cites Learning to walk in minutes using massively parallel deep reinforcement learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Learning to walk in minutes using massively parallel deep reinforcement learning

Reference 52

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Observation 1bcfc36c-c96d-47f0-9cfd-b7b6296414bf · outbound

This paper cites Learning to walk in minutes using massively parallel deep reinforcement learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Learning to walk in minutes using massively parallel deep reinforcement learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:59.267385Z

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Observation 14e36ee4-2e16-4c8a-beef-43e2603acf4e · outbound

This paper cites The phenomenon of policy churn.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks The phenomenon of policy churn

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:59.252411Z

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Observation c368cf97-041b-48af-bdb6-f298ceda84f4 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 55

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Observation 4ef8e964-a9cf-43e6-a3eb-b89e641d65c9 · outbound

This paper cites Proximal Policy Optimization Algorithms.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Proximal Policy Optimization Algorithms

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:55.920896Z

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Observation 4f289300-b452-4889-b3b7-7c01764726e7 · outbound

This paper cites S., Courville, A., Bellemare, M.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks S., Courville, A., Bellemare, M

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:59.238508Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0cb3a222-bdeb-45d7-85e4-a616a2991a65 · outbound

This paper cites J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., and Dahl, G.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., and Dahl, G

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:59.178129Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation abe86cf0-bd5f-494b-824c-013f135ba93e · outbound

This paper cites Sapg: split and aggregate policy gradients.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Sapg: split and aggregate policy gradients

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:58.997221Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0a4672f4-237c-4c86-be5b-1137a9a46641 · outbound

This paper cites S., and Evci, U.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks S., and Evci, U

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:56.179277Z

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Observation 68e03082-6f50-4286-8bfc-2cd3a5b24c09 · outbound

This paper cites Accelerated Methods for Deep Reinforcement Learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Accelerated Methods for Deep Reinforcement Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:56.234793Z

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Observation 11bd33a0-7a25-481c-8618-cf7046543f65 · outbound

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The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:10:58.802545Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 86306a07-ffab-4a05-86f8-92240486037e · outbound

This paper cites S., McAllester, D., Singh, S., and Mansour, Y.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks S., McAllester, D., Singh, S., and Mansour, Y

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:58.587494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 435e7a6c-2469-4ed2-a692-fdb08da1f265 · outbound

This paper cites A., Fedus, W., Machado, M.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks A., Fedus, W., Machado, M

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:58.401749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 747a0e0b-7143-4187-9cad-6ebcc4c07e27 · outbound

This paper cites A., Agarwal, R., Farebrother, J., Courville, A., and Bellemare, M.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks A., Agarwal, R., Farebrother, J., Courville, A., and Bellemare, M

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:58.128727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ffe6d0ce-047f-4e73-b353-c94cfea9a4e8 · outbound

This paper cites and Drake Jr, F.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks and Drake Jr, F

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:56.559420Z

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Observation c53b78ef-7ec3-40bc-b3e8-b042d2b9e34d · outbound

This paper cites M., Mathieu, M., Dudzik, A., Chung, J., Choi, D.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks M., Mathieu, M., Dudzik, A., Chung, J., Choi, D

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:56.624491Z

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Observation 34afcb2c-3a8a-4423-895f-062d333cc788 · outbound

This paper cites Envpool: A highly parallel reinforcement learning environment execution engine.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Envpool: A highly parallel reinforcement learning environment execution engine

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:10:57.809245Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 979169ea-fcfe-42bb-964e-2503c335cffb · outbound

This paper cites Harnessing Structures for Value-Based Planning and Reinforcement Learning.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks Harnessing Structures for Value-Based Planning and Reinforcement Learning

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:10:57.065060Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4f3e9417-32e9-4233-8388-ff87f73ba5cd · outbound

This paper cites write newline.

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks write newline

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T11:10:56.813881Z

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Pith citing papers

Observation b41a1983-3b77-479e-b2b4-547f72c1f91f · inbound

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies cites this paper.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-06T04:39:12.305554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1e5e70de-29c6-4ecc-b668-a7a735505cbe · inbound

X-NavDP: Generalizing Navigation Diffusion Policy to Novel Behavior and Embodiments with Group Q-score Reweighted Matching cites this paper.

X-NavDP: Generalizing Navigation Diffusion Policy to Novel Behavior and Embodiments with Group Q-score Reweighted Matching The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-31T03:42:27.912475Z

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