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Paper Citation Record · LEDGER

Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2108.03952.

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

pith.paper-citation-record.v1
2108.03952 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:26:24.406865Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T07:41:03.347749Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 55955612-bd16-4d33-84f8-1ac037f7901b · inbound

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning cites this paper.

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-10T21:26:24.406865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:26:24.406865Z digest=sha256:9f549aac44f71c4494049ebb77282c0a84fac3e1e7e037bff17ddfa4622a6058

Observation 0ecfdc70-cefc-4153-839e-36efa0cf0e30 · inbound

Constraint-Aware Reinforcement Learning via Adaptive Action Scaling cites this paper.

Constraint-Aware Reinforcement Learning via Adaptive Action Scaling Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-18T07:41:03.350105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-18T07:36:28.841586Z digest=sha256:179aa3e30a544c25775f2f3d3248ec6efa2e5ad940efa6197c530391b2b0d4b8