Paper Citation Record · LEDGER

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.18719.

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

pith.paper-citation-record.v1
2607.18719 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:36:01.708812Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

22 of 22 outbound references displayed

verified exact0
verified fuzzy0
unresolved22
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c4d897d4-29c6-490d-933d-65ab9b10ade1 · outbound

This paper cites Learning to Understand Goal Specifications by Modelling Reward.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Learning to Understand Goal Specifications by Modelling Reward

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:58.826822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a70a5ff2-aa3f-430b-a609-d656c87e30cf · outbound

This paper cites Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:58.982586Z

Source-reported events for the cited work

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Observation 59a9f9a9-83d8-49be-b80a-2a714e789019 · outbound

This paper cites Implicit quantile networks for distributional reinforcement learning,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Implicit quantile networks for distributional reinforcement learning,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:59.112881Z

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Unavailable: canonical work link unavailable.

Observation b2633022-5b90-413b-b976-8475aff39cd3 · outbound

This paper cites Speaker- follower models for vision-and-language navigation,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Speaker- follower models for vision-and-language navigation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:59.280736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4021501e-a9e8-4c9a-98de-ca07834f5eba · outbound

This paper cites Hierarchical program- triggered reinforcement learning agents for automated driving,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Hierarchical program- triggered reinforcement learning agents for automated driving,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:59.426740Z

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Unavailable: canonical work link unavailable.

Observation e071932b-aaae-4483-8464-7346bb70bca1 · outbound

This paper cites Cirl: Controllable imitative reinforcement learning for vision-based self-driving,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Cirl: Controllable imitative reinforcement learning for vision-based self-driving,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:59.585344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bc79385-cb73-4ba4-a861-3c1b1bc472e7 · outbound

This paper cites Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:59.717941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38e4d5d0-f80e-4f4a-946e-af85c2f85e83 · outbound

This paper cites Analysis of coordinated behavior structures with multi-agent deep reinforcement learning,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Analysis of coordinated behavior structures with multi-agent deep reinforcement learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:59.850965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b2af1ed-1193-494d-94db-78bc5344b516 · outbound

This paper cites Interpretability for conditional co- ordinated behavior in multi-agent reinforcement learning,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Interpretability for conditional co- ordinated behavior in multi-agent reinforcement learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:59.968014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9f12557-d7f7-48d0-badb-15f5d24e0eb6 · outbound

This paper cites Strategy-following multi-agent deep reinforcement learning through external high-level instruction,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Strategy-following multi-agent deep reinforcement learning through external high-level instruction,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:00.142659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fec71b9-d086-4670-876d-db3aba18f3f6 · outbound

This paper cites an unresolved cited work.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:00.334122Z

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Observation 4426bf42-7569-4684-8472-cc6802b87a8c · outbound

This paper cites EPOpt: Learning Robust Neural Network Policies Using Model Ensembles.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:00.510306Z

Source-reported events for the cited work

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Observation 8d4bfb62-2df5-4099-b524-3473ed7501de · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents A reduction of imitation learning and structured prediction to no-regret online learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:00.635131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f29c1eb2-9923-48e6-9b69-b4c50c397dd7 · outbound

This paper cites The StarCraft Multi-Agent Challenge.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents The StarCraft Multi-Agent Challenge

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:00.853580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0fb071e-d947-4772-9e45-7ec5f919a554 · outbound

This paper cites Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:00.966006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da24faac-f0cc-4686-8792-e6507573961c · outbound

This paper cites Task offloading and trajectory scheduling for uav-enabled mec networks: An madrl algorithm with prioritized experience replay,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Task offloading and trajectory scheduling for uav-enabled mec networks: An madrl algorithm with prioritized experience replay,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:01.030417Z

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Unavailable: canonical work link unavailable.

Observation 457f68cf-5c46-487a-8daa-7713383c56f8 · outbound

This paper cites Program guided agent,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Program guided agent,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:01.180288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcbbf0e7-5517-4b61-bfca-8cffc81f6c96 · outbound

This paper cites Attention is all you need,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Attention is all you need,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:01.310222Z

Source-reported events for the cited work

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Observation 409aac75-7301-4958-ac92-270275af07e9 · outbound

This paper cites Understanding natural language,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Understanding natural language,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:01.389483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4514b892-c853-4ff2-9209-44fe0a5d6d96 · outbound

This paper cites Toward human-in-the-loop ai: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Toward human-in-the-loop ai: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:01.464775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf0ee00-3225-4951-ae76-e3b2370b72df · outbound

This paper cites Program synthesis guided reinforcement learning for partially observed environments,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Program synthesis guided reinforcement learning for partially observed environments,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:01.597241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c66c4dc-028f-4165-8d36-acdb504def6b · outbound

This paper cites Joint sensing and communication optimization in target-mounted stars-assisted vehicular networks: A madrl approach,.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents Joint sensing and communication optimization in target-mounted stars-assisted vehicular networks: A madrl approach,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T14:36:01.708812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.