Paper Citation Record · LEDGER

Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2109.11251.

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

pith.paper-citation-record.v1
2109.11251 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:23:07.654898Z

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

83
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 1cff9fb9-eb41-4802-a372-4046bfc8210e · inbound

Light Aircraft Game : Basic Implementation and training results analysis cites this paper.

Light Aircraft Game : Basic Implementation and training results analysis Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:23:07.654898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9f7b077-a641-4f1e-b536-eba274abbe39 · inbound

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient cites this paper.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T17:47:37.242154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd55247-3864-45aa-92d3-e5006119891a · inbound

Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies cites this paper.

Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T01:06:57.409927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 924df6fc-5af8-4b3f-8a73-96966cc68a29 · inbound

Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach cites this paper.

Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T20:31:02.434225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce1cbd52-a50d-43df-9d3c-f212f995948f · inbound

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning cites this paper.

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:45:29.793674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation acc98b05-d732-4449-9d73-9c2f0350a4cc · inbound

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning cites this paper.

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T05:37:38.440274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d29723a-b446-4a5e-9b8e-cae77fc18570 · inbound

Topology-Driven Anti-Entanglement Control for Soft Robots cites this paper.

Topology-Driven Anti-Entanglement Control for Soft Robots Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:41:28.058511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b778fc2f-accf-435a-8f96-be38f0240d6c · inbound

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning cites this paper.

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 122

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:15:05.903774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 401883ea-8851-4bfa-9b3c-1bfcc7509581 · inbound

Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation cites this paper.

Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:12:54.902012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a6b8c58-8480-4861-9f48-66da0251baad · inbound

TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination cites this paper.

TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T18:02:42.323932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 113eb189-443b-40bf-9fcc-d7de7a1804dc · 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 Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T08:47:50.453357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbbcca0b-11cc-4c9c-93b3-d697a1a39799 · inbound

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids cites this paper.

Reference-Free Heterogeneous Multi-Agent Reinforcement Learning for Grid-Friendly Tie-Line Power Shaping in Industrial Microgrids Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:50:11.104720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 235d54c4-9e76-462f-9e21-80b4af10140d · inbound

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning cites this paper.

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-13T03:08:01.590659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d46f04a-1a2d-4c1b-87ba-8bdd023b8cd3 · 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 Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T21:47:09.010614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.