Paper Citation Record · LEDGER

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.02316.

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

pith.paper-citation-record.v1
2412.02316 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:41:05.156238Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

28 of 28 outbound references displayed

verified exact1
verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef848971-afc7-4360-a390-d3ef77df7475 · outbound

This paper cites The New Plastics Economy: Rethinking the future of plastics,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning The New Plastics Economy: Rethinking the future of plastics,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.657403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 596e9926-cda3-494c-b00c-3e436b0aa676 · outbound

This paper cites A survey on multi-robot systems,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning A survey on multi-robot systems,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.641593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6f5b98fb-af9e-48be-b468-42d9272dff92 · outbound

This paper cites Cooperative heterogeneous multi- robot systems: A survey,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Cooperative heterogeneous multi- robot systems: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.622513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2aae8f83-321b-45bd-86d9-396850e1f94e · outbound

This paper cites A survey and critique of multiagent deep reinforcement learning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning A survey and critique of multiagent deep reinforcement learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.604757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e47ab2eb-137a-4609-9d18-491da3386357 · outbound

This paper cites Learning- based methods for adaptive informative path planning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Learning- based methods for adaptive informative path planning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.585692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 661cab09-5159-4aff-8ad3-16242301d405 · outbound

This paper cites Aquafel-pso: An informative path planning for water resources monitoring using autonomous surface vehicles based on multi-modal pso and federated learning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Aquafel-pso: An informative path planning for water resources monitoring using autonomous surface vehicles based on multi-modal pso and federated learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.568370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e1aea49d-0592-4287-91c9-2c74427168ab · outbound

This paper cites Water quality online modeling using multi-objective and multi-agent bayesian optimization with region partitioning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Water quality online modeling using multi-objective and multi-agent bayesian optimization with region partitioning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.552211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8b070143-376e-4456-8db1-def44ce5bd04 · outbound

This paper cites Deep reinforcement learning algorithms for path planning domain in grid-like environment,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Deep reinforcement learning algorithms for path planning domain in grid-like environment,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.536554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f4422bdf-475b-414f-a731-79f691917d43 · outbound

This paper cites Deep reinforcement learning with dynamic graphs for adaptive informative path planning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Deep reinforcement learning with dynamic graphs for adaptive informative path planning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.520473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e684d138-b0b8-4e6a-bded-981ca3ef37cf · outbound

This paper cites Dynamic path planning of unknown environment based on deep reinforcement learning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Dynamic path planning of unknown environment based on deep reinforcement learning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.504194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d94856fb-b8e8-4495-9b24-0078f55dab5c · outbound

This paper cites Deep reinforcement multiagent learning framework for infor- mation gathering with local gaussian processes for water monitoring,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Deep reinforcement multiagent learning framework for infor- mation gathering with local gaussian processes for water monitoring,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.488297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d6e4376-79a2-408a-8881-72128fa3c992 · outbound

This paper cites Multi-robot path planning based on a deep reinforcement learning dqn algorithm,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Multi-robot path planning based on a deep reinforcement learning dqn algorithm,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.472358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 746cb41a-69b1-4339-bdfb-15f3089d24ef · outbound

This paper cites Collision avoidance for an unmanned surface ve- hicle using deep reinforcement learning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Collision avoidance for an unmanned surface ve- hicle using deep reinforcement learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.457140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0701920c-1340-4e7c-8dd1-40ab25f87236 · outbound

This paper cites Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.440256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53e5576e-4b69-4d34-8b81-a9830b202d1d · outbound

This paper cites Informative deep reinforcement path planning for heterogeneous au- tonomous surface vehicles in large water resources,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Informative deep reinforcement path planning for heterogeneous au- tonomous surface vehicles in large water resources,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.422319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c5bfd7d4-3a64-440c-b73b-91c0793dcf5a · outbound

This paper cites Heterogeneous multi-agent deep reinforce- ment learning for traffic lights control,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Heterogeneous multi-agent deep reinforce- ment learning for traffic lights control,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.403897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07825623-e2cc-4011-9409-d168024760d1 · outbound

This paper cites Asymmetric self-play-enabled intelligent heterogeneous multi- robot catching system using deep multiagent reinforcement learning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Asymmetric self-play-enabled intelligent heterogeneous multi- robot catching system using deep multiagent reinforcement learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.386381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b313fd77-5ba2-4fc1-b74e-6afe5c6be266 · outbound

This paper cites Unmanned floating waste collecting robot,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Unmanned floating waste collecting robot,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.368521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5d24ac2-1be5-4872-9ff4-9bb94637b261 · outbound

This paper cites Development of water surface mobile garbage collector robot,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Development of water surface mobile garbage collector robot,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.352364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2a42da2d-d9e4-4509-bbab-6828b4fab80f · outbound

This paper cites Automatic collaborative water surface coverage and cleaning strategy of UA V and USVs,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Automatic collaborative water surface coverage and cleaning strategy of UA V and USVs,

Reference 20

Resolution: verified exact; doi, observed 2026-08-11T23:41:05.199229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 74310d48-6258-41b1-965a-820af949ff3d · outbound

This paper cites Flow: A dataset and bench- mark for floating waste detection in inland waters,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Flow: A dataset and bench- mark for floating waste detection in inland waters,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.336570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c6025813-f8f4-4730-8087-ba1caefedea9 · outbound

This paper cites Deep reinforcement learning with double q-learning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Deep reinforcement learning with double q-learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.320498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2bcaf1e7-25ea-40f5-b7fc-7a9368427262 · outbound

This paper cites Bellman, Dynamic Programming.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Bellman, Dynamic Programming

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.302757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 14845e2f-2c5f-40f1-90fd-16f5d2e94d27 · outbound

This paper cites Prioritized experience replay,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Prioritized experience replay,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.285505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e9b97b96-221e-4c3d-8051-9418e4049e5b · outbound

This paper cites Dueling network architectures for deep reinforcement learning,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Dueling network architectures for deep reinforcement learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.268108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1dfd2af1-e2cc-4e16-bd54-4f9701fc44fb · outbound

This paper cites Reward (mis)design for autonomous driving,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Reward (mis)design for autonomous driving,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.251295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation acdb7612-10c1-4f0b-b002-a87c6acd77e5 · outbound

This paper cites Designing Rewards for Fast Learning.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Designing Rewards for Fast Learning

Reference 27

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Source-reported events for the cited work

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Observation ac82a55e-0760-4ffd-8d15-3f544ebad055 · outbound

This paper cites Greed is good: Near-optimal submodular maximization via greedy optimization,.

Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning Greed is good: Near-optimal submodular maximization via greedy optimization,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:41:05.235397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.