Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.09989.

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

pith.paper-citation-record.v1
2507.09989 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:47:39.488108Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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verified fuzzy24
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External citation measurements

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Outbound references

Observation a1fb07d7-507a-48af-9ee5-513978531797 · outbound

This paper cites Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs

Reference 1

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Observation 8c506b9e-95db-4b15-b18b-0e8c687d69d7 · outbound

This paper cites Proceedings of the International Conference on Learning Representations (2022).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Proceedings of the International Conference on Learning Representations (2022)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.438220Z

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Observation 61012a7c-78bf-4851-98d2-78bf93e6df10 · outbound

This paper cites Pro- ceedings of the 2023 International Conference on Autonomous Agents and Multiagent Sys- tems pp.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Pro- ceedings of the 2023 International Conference on Autonomous Agents and Multiagent Sys- tems pp

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.422021Z

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Observation 78901d04-639e-469c-bbb3-88b198cb3e12 · outbound

This paper cites Joint European Conference on Machine Learning and Knowledge Discovery in Databases pp.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Joint European Conference on Machine Learning and Knowledge Discovery in Databases pp

Reference 4

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Observation a28de0a2-a975-4e82-8050-52db1790c0dd · outbound

This paper cites International Joint Conference on Artificial Intelligence (2024).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient International Joint Conference on Artificial Intelligence (2024)

Reference 5

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

This paper cites Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning.

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

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Observation 063b6800-0521-46b1-85f8-6a051fef939d · outbound

This paper cites the Thirty-Eighth Annual Conference on Neural Information Pro- cessing Systems (NeurIPS) (2024).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient the Thirty-Eighth Annual Conference on Neural Information Pro- cessing Systems (NeurIPS) (2024)

Reference 7

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Observation 5d2937e1-04a3-4716-b108-7b0d664281a3 · outbound

This paper cites CoRR (2023).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient CoRR (2023)

Reference 8

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Observation 380c99e2-9524-4959-b545-1389d5e469e7 · outbound

This paper cites The Twelfth International Conference on Learning Representations (2024).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient The Twelfth International Conference on Learning Representations (2024)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.323345Z

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Observation 7c7dd837-0402-40a1-aaeb-442128e50d50 · outbound

This paper cites The Twelfth International Conference on Learning Representations (2024) Title Suppressed Due to Excessive Length 13.

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.307050Z

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Observation eb079445-1324-4aa5-9a0a-78e13167b24a · outbound

This paper cites Neural Information Processing Systems (NIPS) (2017).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Neural Information Processing Systems (NIPS) (2017)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.289219Z

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Observation fb8c8ab5-4842-438f-a72f-a58e8421c799 · outbound

This paper cites Advances in Neural Information Processing Systems 32 (2019).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Advances in Neural Information Processing Systems 32 (2019)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.270317Z

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Observation 439d077f-ba02-49f9-a46e-99ac3d29cc92 · outbound

This paper cites Springer (2016).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Springer (2016)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.249864Z

Source-reported events for the cited work

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Observation a7c27d62-01f7-4309-92ce-878c65b67101 · outbound

This paper cites Applied Intelligence 53(4), 4483–4498 (2023).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Applied Intelligence 53(4), 4483–4498 (2023)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.235075Z

Source-reported events for the cited work

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Observation 76057f61-0aa5-49b1-b1de-78c6a887666c · outbound

This paper cites The Journal of Machine Learning Research 21(1), 7234–7284 (2020).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient The Journal of Machine Learning Research 21(1), 7234–7284 (2020)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.219283Z

Source-reported events for the cited work

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Observation 6d3bb8c5-9eb2-482c-96b9-6d21f21b69eb · outbound

This paper cites FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging

Reference 16

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Observation 3b5161f9-e1ee-498d-9936-389d9f3020d7 · outbound

This paper cites Neural Computing and Applications 35(27), 19765–19781 (2023).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Neural Computing and Applications 35(27), 19765–19781 (2023)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.199924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dffaa61c-db81-4f8f-81ab-fe11b132a75b · outbound

This paper cites Advances in Neural Information Processing Systems 35, 16509–16521 (2022).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Advances in Neural Information Processing Systems 35, 16509–16521 (2022)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.173531Z

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Observation 41ef0326-dcc2-416f-bc7c-779c1a4dc774 · outbound

This paper cites Theses and Dissertations.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Theses and Dissertations

Reference 19

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Observation 63631aa8-cc7f-417e-939a-6a96e0d49dfa · outbound

This paper cites The International FLAIRS Conference Proceedings, 35 (2022).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient The International FLAIRS Conference Proceedings, 35 (2022)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.124489Z

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Observation 5e89f877-1b8c-44bc-8037-515e86d2fd20 · outbound

This paper cites IEEE Transactions on Vehicular Technology69(8), 8243–8256 (2020).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient IEEE Transactions on Vehicular Technology69(8), 8243–8256 (2020)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.102216Z

Source-reported events for the cited work

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Observation d5642dde-b820-4321-b5c4-1219b425b296 · outbound

This paper cites Designing Heterogeneous LLM Agents for Financial Sentiment Analysis.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Designing Heterogeneous LLM Agents for Financial Sentiment Analysis

Reference 22

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Observation 65dea1ea-b486-4dfd-8204-73e9e4c6c55a · outbound

This paper cites 2021 IEEE International Confer- ence on Systems, Man, and Cybernetics (SMC) pp.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient 2021 IEEE International Confer- ence on Systems, Man, and Cybernetics (SMC) pp

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.076193Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c86db028-91ff-41eb-89c9-caf595f0452f · outbound

This paper cites AIAA Scitech 2019 Forum p.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient AIAA Scitech 2019 Forum p

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.053893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 819dfc1c-9d73-4f90-98ea-28967e163a33 · outbound

This paper cites The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

Reference 25

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Observation a20098aa-3adb-4c03-aa7f-5b1476f29c16 · outbound

This paper cites Applied Sciences 15(5), 2580 (2025).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Applied Sciences 15(5), 2580 (2025)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.032904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 42be7f25-7db7-4388-a20f-ffa157071d99 · outbound

This paper cites Complex & Intelligent Systems pp.

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Complex & Intelligent Systems pp

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:40.010555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a71bfb5-a906-4ae5-9763-2a2ed60b78d7 · outbound

This paper cites Neurocomputing 411, 206–215 (2020).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Neurocomputing 411, 206–215 (2020)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:39.991068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 998a9b7f-804e-4023-804a-b80a1f1d82d4 · outbound

This paper cites Autonomous Agents and Multi-Agent Systems 38(1), 4 (2024).

Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient Autonomous Agents and Multi-Agent Systems 38(1), 4 (2024)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:47:39.969754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.