Paper Citation Record · LEDGER

A Review of Nine Physics Engines for Reinforcement Learning Research

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.08590.

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

pith.paper-citation-record.v1
2407.08590 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:55:16.987192Z

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

2
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 23e09a32-acca-4d08-8a51-15c09a140c70 · inbound

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research cites this paper.

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research A Review of Nine Physics Engines for Reinforcement Learning Research

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:16.987192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ebd1166-bc00-4c68-8707-e0fa8b6ec1b8 · inbound

Physically Accurate Rigid-Body Dynamics in Particle-Based Simulation cites this paper.

Physically Accurate Rigid-Body Dynamics in Particle-Based Simulation A Review of Nine Physics Engines for Reinforcement Learning Research

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:09:57.925236Z

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 7f41a500-d329-4380-9f8d-5372725acc24 · inbound

Trajectory-based actuator identification via differentiable simulation cites this paper.

Trajectory-based actuator identification via differentiable simulation A Review of Nine Physics Engines for Reinforcement Learning Research

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:05.002598Z

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 38bb44bb-622d-4032-a7c5-7269345c12ba · inbound

Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters cites this paper.

Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters A Review of Nine Physics Engines for Reinforcement Learning Research

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-08T19:34:04.835552Z

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 4d4d3f78-d5b8-4b72-95f6-e6d183f4c844 · inbound

Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters cites this paper.

Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters A Review of Nine Physics Engines for Reinforcement Learning Research

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T00:25:09.180584Z

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 e35aec0b-aa86-400e-8e18-766ad1401095 · inbound

Co-Design of Aeroelastic Systems with Deep Reinforcement Learning cites this paper.

Co-Design of Aeroelastic Systems with Deep Reinforcement Learning A Review of Nine Physics Engines for Reinforcement Learning Research

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-02T00:09:36.916334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.