Paper Citation Record · LEDGER

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots

As of 20 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2507.10030.

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

pith.paper-citation-record.v1
2507.10030 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:46:56.087035Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

16 of 16 outbound references displayed

verified exact2
verified fuzzy2
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 151c77bb-7b15-4d05-bad5-e0d2c0d18d5b · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:58.347079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cceebf64-9cfa-4592-90ba-8493f1a70e71 · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:58.152433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 22b11ab1-2ca6-47b9-b43c-00e08a1e1b45 · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:58.016373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 26290483-f9c1-47c2-b2b4-7987d8185b95 · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:57.871640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e5d8c68d-e6fd-4b19-8dfb-20e3a19219d1 · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:57.740503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a195ced-82f7-41bf-b16a-784a9e906295 · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:57.596600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78cc31e8-007a-4ec9-a790-1fc287732d83 · outbound

This paper cites and Ostermeier, A.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots and Ostermeier, A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:57.466802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9bfbdc37-3f29-4a93-a8d7-d74f2d7557f8 · outbound

This paper cites Continuous control with deep reinforcement learning.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Continuous control with deep reinforcement learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:55.037450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 262d8575-a40b-418e-9b81-32f28c9b0540 · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:57.337594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b53a6565-ffc1-4402-824e-88fca967ffd9 · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:57.188080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 34afc2d2-5929-444b-bd8d-ad19acaa85cf · outbound

This paper cites Learning control of underactuated double pendulum with Model-Based Reinforcement Learning.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Learning control of underactuated double pendulum with Model-Based Reinforcement Learning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:46:56.579644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1dfd7134-5027-475d-ac6d-30d4f937cb7c · outbound

This paper cites an unresolved cited work.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:46:57.054520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77ba26fa-0c23-4a5a-9d39-2b661d0f9e35 · outbound

This paper cites ai olympics with realaigym.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots ai olympics with realaigym

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:46:56.902544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 64f16195-1003-4f53-b714-de84a2bb7866 · outbound

This paper cites Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:46:56.342824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 023bd868-5a55-4c62-b4f1-a27a46a1fd89 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots , " * write output.state after.block = add.period write newline

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:55.982908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7df4f830-549d-407b-84f9-ec14d54d096e · outbound

This paper cites write newline.

Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots write newline

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:46:56.087035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.