Paper Citation Record · LEDGER

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

As of 30 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2006.07869.

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

pith.paper-citation-record.v1
2006.07869 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-30T06:33:22.917629+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T07:38:02.368884Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

Reference resolution

0 of 0 outbound references displayed

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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 d1922028-8854-4da9-97d6-ebce5426a694 · inbound

Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning cites this paper.

Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:17:30.907248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 9a029f53-bc49-464a-8c19-859f210dd09d · 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 Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-19T01:06:57.465247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation e196b80d-e2f4-4a5a-a4ad-8a288b05d8f0 · inbound

AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning cites this paper.

AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:20:11.904137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 79035392-af4c-4926-ab07-8d772f861638 · inbound

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 103

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:13:15.663980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation c61a811d-1cb9-4766-8d51-a4b0b2a2f09d · inbound

From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments cites this paper.

From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:23:27.178151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 61dcb148-354e-44ae-95e4-2426dad23a5f · inbound

Scalable Neighborhood-Based Multi-Agent Actor-Critic cites this paper.

Scalable Neighborhood-Based Multi-Agent Actor-Critic Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:23:38.040503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation f5a7f3e5-60c2-4d85-98c8-10252cef0166 · inbound

SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems cites this paper.

SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:41:17.844145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 157ec0c9-259b-4e87-beed-e437935ab5cb · inbound

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning cites this paper.

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:27.325451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation c69e52a9-7626-4c08-b9f2-7dc24462842c · inbound

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning cites this paper.

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:39:10.780951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 7d449fc2-f463-420e-a44e-d0e961a7747f · inbound

Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning cites this paper.

Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:51:27.936155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation ff9f91cb-790d-4c46-b7d5-bbddcb5401cb · inbound

DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games cites this paper.

DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:32:59.348331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 770d6fbe-f2d8-4eeb-9ff0-e07cbc2b3ea7 · inbound

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

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 123

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T22:15:05.801534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation e1cb1706-5baf-4eb1-a04f-d320dfcec8e3 · inbound

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning cites this paper.

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:55:00.849588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation c4ee1119-6609-4df4-8ed3-0d9c52a498cc · inbound

CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control cites this paper.

CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T21:03:58.925631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 2b652c66-58f8-49d2-b7ac-07efb65f00d1 · inbound

Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning cites this paper.

Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T07:38:02.368884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e311621e-b17b-4d73-ae61-dd336cf0c76e · 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 Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T19:30:07.589674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.