Paper Citation Record · LEDGER

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.08222.

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

pith.paper-citation-record.v1
2505.08222 v3

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:06:12.218071Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

31 of 31 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50a92ce3-536c-4180-9b72-7d58182a8de0 · outbound

This paper cites Frontal dynamics in the Alboran sea: 1. Coherent 3D pathways at the Almeria- Oran front using underwater glider observations.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Frontal dynamics in the Alboran sea: 1. Coherent 3D pathways at the Almeria- Oran front using underwater glider observations

Reference 1

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Observation 9befb08e-df0c-45d7-80ce-0524ca754fb1 · outbound

This paper cites Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.747883Z

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Observation 6dc47c50-cd87-4f3b-8c12-cad4c71d7183 · outbound

This paper cites A system of coordinated au- tonomous robots for Lagrangian studies of microbes in the oceanic deep chlorophyll maximum.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles A system of coordinated au- tonomous robots for Lagrangian studies of microbes in the oceanic deep chlorophyll maximum

Reference 3

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Observation 3614d45d-9175-4df1-b03a-5379de742d25 · outbound

This paper cites Spatial ecology of Norway lobster Nephrops norvegicus in Mediterranean deep-water en- vironments: implications for designing no-take marine reserves.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Spatial ecology of Norway lobster Nephrops norvegicus in Mediterranean deep-water en- vironments: implications for designing no-take marine reserves

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.718783Z

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Observation 88f86972-cbf3-43ee-aa19-68dde73259c6 · outbound

This paper cites Underwater sensor networks: applications, advances and challenges.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Underwater sensor networks: applications, advances and challenges

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.703998Z

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Observation b874b3e7-30c3-4c5a-90ec-c005c59f4e75 · outbound

This paper cites Characterizing snow crab (Chionoe- cetes opilio) movements in the Sydney Bight (Nova Sco- tia, Canada): a collaborative approach using multiscale acoustic telemetry.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Characterizing snow crab (Chionoe- cetes opilio) movements in the Sydney Bight (Nova Sco- tia, Canada): a collaborative approach using multiscale acoustic telemetry

Reference 6

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Observation 08d706a9-fc8f-4b13-885d-0bc0b96d4576 · outbound

This paper cites Dynamic robotic tracking of under- water targets using reinforcement learning.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Dynamic robotic tracking of under- water targets using reinforcement learning

Reference 7

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Observation 3b7eebf7-e889-4177-b53e-c0eb0260c6db · outbound

This paper cites Multi-AUV cooperative underwa- ter multi-target tracking based on dynamic-switching- enabled multi-agent reinforcement learning.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Multi-AUV cooperative underwa- ter multi-target tracking based on dynamic-switching- enabled multi-agent reinforcement learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.659267Z

Source-reported events for the cited work

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Observation 3a2693c7-1be2-4af9-9796-5667880c8576 · outbound

This paper cites Secure and cooperative target tracking via AUV swarm: A reinforcement learning approach.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Secure and cooperative target tracking via AUV swarm: A reinforcement learning approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.643992Z

Source-reported events for the cited work

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Observation d32047c1-ab4a-4b5b-8f90-9464daea235d · outbound

This paper cites From Concept to Field Tests: Accelerated Development of Multi-AUV Mis- sions Using a High-Fidelity Faster-than-Real-Time Simulator.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles From Concept to Field Tests: Accelerated Development of Multi-AUV Mis- sions Using a High-Fidelity Faster-than-Real-Time Simulator

Reference 10

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Observation 66ddc394-f4d3-470b-8621-bee7cc84427a · outbound

This paper cites Robot Operating System 2: Design, architecture, and uses in the wild.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Robot Operating System 2: Design, architecture, and uses in the wild

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.627971Z

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Observation 8043b224-023b-4d7e-9e30-b2f18e949108 · outbound

This paper cites JaxMARL: Multi-Agent RL Environments and Algorithms in JAX.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Reference 12

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Observation 2ce21840-9a22-4c33-b1e9-4322c59d2825 · outbound

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

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

Reference 13

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Observation 7de2d379-f736-4560-8235-f151a59f52ea · outbound

This paper cites Synchronization of Multiagent Systems Using Event-Triggered and Self-Triggered Broadcasts.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Synchronization of Multiagent Systems Using Event-Triggered and Self-Triggered Broadcasts

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.612661Z

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Observation bb686cee-eb18-4328-8ecd-0e1b3d538968 · outbound

This paper cites TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.596848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3205f401-d295-4fbe-a784-a9a4e2fd2a95 · outbound

This paper cites Scalable multi-agent rein- forcement learning through intelligent information ag- gregation.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Scalable multi-agent rein- forcement learning through intelligent information ag- gregation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.581354Z

Source-reported events for the cited work

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Observation bfcc617d-05e5-432d-9d19-cb816b5cd996 · outbound

This paper cites Graph neural network- based multi-agent reinforcement learning for resilient distributed coordination of multi-robot systems.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Graph neural network- based multi-agent reinforcement learning for resilient distributed coordination of multi-robot systems

Reference 17

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Observation c834634e-a5c5-47ee-910d-37f22457e784 · outbound

This paper cites Transformer-based multi-agent rein- forcement learning for generalization of heterogeneous multi-robot cooperation.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Transformer-based multi-agent rein- forcement learning for generalization of heterogeneous multi-robot cooperation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.551503Z

Source-reported events for the cited work

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Observation bc8394e3-1868-4a28-b5cf-1e5616a5710a · outbound

This paper cites UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 54eef67f-4d10-4d23-83cc-dec52ff2cb09 · outbound

This paper cites Multi-agent reinforcement learning is a sequence modeling problem.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Multi-agent reinforcement learning is a sequence modeling problem

Reference 20

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1929af2c-02a6-4c64-9c04-c01e187fcae2 · outbound

This paper cites Automatic curriculum learning for large-scale cooperative multiagent systems.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Automatic curriculum learning for large-scale cooperative multiagent systems

Reference 21

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Observation c389d795-34b1-4f4b-819b-880b2175348f · outbound

This paper cites From few to more: Large-scale dynamic multiagent curriculum learning.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles From few to more: Large-scale dynamic multiagent curriculum learning

Reference 22

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Observation ae7e684c-44ab-4902-aaec-265b08bc7b53 · outbound

This paper cites Robust Range-Only Beacon Localization.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Robust Range-Only Beacon Localization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.472534Z

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Observation 93c9b132-f020-4f08-a65e-e7352e661015 · outbound

This paper cites Observ- ability based control in range-only underwater vehicle localization.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Observ- ability based control in range-only underwater vehicle localization

Reference 24

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Observation 0f592130-772d-4751-b1ec-1708261714bb · outbound

This paper cites Positioning and navigation sys- tems for robotic underwater vehicles.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Positioning and navigation sys- tems for robotic underwater vehicles

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.455987Z

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Observation 102126df-7ecf-40fb-8a57-e2ba8ab40dfc · outbound

This paper cites Observ- ability based control in range-only underwater vehicle localization.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Observ- ability based control in range-only underwater vehicle localization

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.440043Z

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Observation e30eb1bb-4147-49b2-b485-93ddd6e6f5dd · outbound

This paper cites Robust range-only beacon localization.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Robust range-only beacon localization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:06:12.423995Z

Source-reported events for the cited work

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Observation a75c64c1-dc0b-4935-875f-66c5a283f0aa · outbound

This paper cites Springer, 2016.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Springer, 2016

Reference 28

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Observation 4bf18d7e-8cc6-4e6d-8993-1403c6176729 · outbound

This paper cites QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Reference 29

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This paper cites Proximal Policy Optimization Algorithms.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Proximal Policy Optimization Algorithms

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:06:12.213775Z

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Observation ca7628d2-bd2c-4038-abc2-85d805080b24 · outbound

This paper cites Simplifying Deep Temporal Difference Learning.

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles Simplifying Deep Temporal Difference Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:06:12.218071Z

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Pith citing papers

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