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Paper Citation Record · LEDGER

Multi-Agent Constrained Policy Optimisation

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2110.02793.

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

pith.paper-citation-record.v1
2110.02793 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:35:49.243705Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:30:07.600849Z

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 fd7d548f-198e-4c9c-b24a-ce595e90522c · inbound

Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control cites this paper.

Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control Multi-Agent Constrained Policy Optimisation

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-09T04:23:17.639752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T04:23:17.639752Z digest=sha256:c0d7c1efdf78e0f088882411cc62619095ee5b97d460ef4fcdd381bc79faa2e2

Observation cd1c5b7b-32eb-4073-a64f-0fea3ec9c907 · inbound

Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL cites this paper.

Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL Multi-Agent Constrained Policy Optimisation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-16T11:35:49.243705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:35:49.243705Z digest=sha256:c45fdbc89d91779a73a5ca7ed9a0fb5cfc8a11bea6612bb00a9771f639efc58c

Observation da4c17d3-6283-4934-9518-ee3b344f93d4 · inbound

Safe Bottom-Up Flexibility Provision from Distributed Energy Resources cites this paper.

Safe Bottom-Up Flexibility Provision from Distributed Energy Resources Multi-Agent Constrained Policy Optimisation

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-16T05:30:27.328620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:30:27.328620Z digest=sha256:64457c2cbc069f80cda60960334fdad8014b4cef03b098beae01fc16dbbfb036

Observation 6f3cbb17-8fd5-40d1-be74-6e14bde515c1 · inbound

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria cites this paper.

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria Multi-Agent Constrained Policy Optimisation

Reference 61

Resolution
verified exact
arxiv_id, observed 2026-07-03T08:47:50.443954Z

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.

source=arxiv_source observed=2026-06-27T10:42:17.852996Z digest=sha256:c19915e17998eb03564a011d234d12f35cadd58b76418b0373ecc97e9837e0be

Observation 62f4797f-e4a3-440d-be67-dd530c454523 · inbound

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning cites this paper.

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning Multi-Agent Constrained Policy Optimisation

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-07-03T19:18:54.932158Z

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.

source=arxiv_source observed=2026-06-27T01:54:19.216553Z digest=sha256:d957786e47b023253eaae22174decc65f1efa80a21934a5381309d4008fcc3bd

Observation 01cfd73e-1f0c-4e94-acb6-08ec9a2d0273 · inbound

Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control cites this paper.

Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control Multi-Agent Constrained Policy Optimisation

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-04T11:39:46.351045Z

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.

source=arxiv_source observed=2026-06-26T07:54:12.950136Z digest=sha256:0714e0707f151f42303f2af68044d378c85708c36f0ef8098977a380e2b5d610

Observation dbd9fe14-791d-41a0-a134-a41219cda2c3 · inbound

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning cites this paper.

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning Multi-Agent Constrained Policy Optimisation

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-07-04T19:30:07.602226Z

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.

source=pdf_text observed=2026-06-25T21:17:28.832301Z digest=sha256:341f809d167c704a49a395cf00527f1b12e61498fcc45d34d53af089be95cc17

Observation 740eee4f-8047-4681-b0f3-df8148330499 · inbound

Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer cites this paper.

Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer Multi-Agent Constrained Policy Optimisation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-01T09:47:32.256371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T09:47:32.256371Z digest=sha256:065a688e55530693cdd09a5cd3c8a33b225ef5ec687fa729b9c5847c79c0fe3d

Observation 1e75df9c-492d-48f6-ab4f-12cc52d18ad7 · inbound

Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints cites this paper.

Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints Multi-Agent Constrained Policy Optimisation

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-04T21:47:08.536662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T21:47:08.536662Z digest=sha256:3a83d3449f8eb90dc904fc99c946f6e7d4fe5f8aba1334196c4badb154b63b2c