Pith. sign in

Paper Citation Record · LEDGER

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2502.02060.

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

pith.paper-citation-record.v1
2502.02060 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:35:05.298663Z

measured 24 of 24 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:37:10.984263Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:37:11.767490Z

Reference resolution

23 of 23 outbound references displayed

  • verified exact0
  • verified fuzzy16
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe7c99d0-b7cd-468d-bddf-d2795f730f04 · outbound

This paper cites Third imo ghg study 2014.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Third imo ghg study 2014

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.537077Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.215224Z digest=sha256:bea2707ee817fe8a8afb3ef084328ff968724ec2d7171bbf237ac5ced1fbc86c

Observation 0cfdcce9-c5f2-4ea6-8b03-6e87c124b76b · outbound

This paper cites Reducing greenhouse gas emissions from ships.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Reducing greenhouse gas emissions from ships

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.526377Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.219273Z digest=sha256:e8634cbf9f5a081d700ba9b8353c14796e6ff2b3821fe893ca703139ac3933c2

Observation c9d49cea-e7f6-494b-8384-35ed0adfd243 · outbound

This paper cites Initial imo strategy on reduction of ghg emissions from ships, 2018.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Initial imo strategy on reduction of ghg emissions from ships, 2018

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.515410Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.223604Z digest=sha256:53a1579cf4f02b63318cfcdf7ce5913004be281f35fff0cc7cfe82f0256dd785

Observation 9df3cee5-0af7-4578-ae75-5a6e7c5259b1 · outbound

This paper cites A Review of Cooperative Multi-Agent Deep Reinforcement Learning.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics A Review of Cooperative Multi-Agent Deep Reinforcement Learning

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-09T13:35:05.226971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:35:05.226971Z digest=sha256:893eae20b9d063d7bef35feebd510085dc47722c6403321e5f830d59b902fe60

Observation 12c372ca-7d3a-478a-96a2-20f9a7a2e40e · outbound

This paper cites Learning to communicate with deep multi-agent reinforcement learning.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Learning to communicate with deep multi-agent reinforcement learning

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.505247Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.231023Z digest=sha256:cfe38025ed215d263988083fab0af3647c3ad39eade75c44ab8e52be095b14b3

Observation 799c5fe9-7922-46d2-9289-ccabb676759b · outbound

This paper cites Cooperative multi-agent learning: The state of the art.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Cooperative multi-agent learning: The state of the art

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.495663Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.236073Z digest=sha256:dabcb18b9a70c71632543ac4138a2934face2498809fdf580da5c8750ae87c60

Observation de03d05c-4e98-4434-b958-de4531278108 · outbound

This paper cites Learning with opponent-learning awareness.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Learning with opponent-learning awareness

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.484892Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.240655Z digest=sha256:14a4654b1efaeb863252605e9a463177d4b3246513af712f73ae83058ba6fd92

Observation f3bdcda4-7f35-4836-84af-0d35d7c69306 · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.474377Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.245066Z digest=sha256:0dde6cf81afe9b8517e0e786cdfc382e89a323f4bc82f955ae0150558c20a4bd

Observation 42f2bac0-71e6-4c31-847b-d3c222489a9a · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-09T13:35:05.249257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:35:05.249257Z digest=sha256:2aaf776a3797ab2861cc5db9ac85bc92724961b43d7d9e4a24e92e4f86b4d819

Observation 9d257809-5eef-4306-99b0-5a4f999a4fd6 · outbound

This paper cites Optimal and approximate q-value functions for decentralized pomdps.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Optimal and approximate q-value functions for decentralized pomdps

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.464408Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.253875Z digest=sha256:088b77f462ceac1f029599d17344383f2f29a05d40eadf7a4eb69ca846695d9e

Observation 739b3408-0a18-4be2-86c6-c66c2a4c3a3c · outbound

This paper cites Cooperative multi-agent control using deep reinforcement learning.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Cooperative multi-agent control using deep reinforcement learning

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.455008Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.257767Z digest=sha256:c81758486e64c7373f20ac776ce6c023253bc564a3004c976219d694036625aa

Observation 6a13ffbf-4ef5-4f42-82bb-4505fa88ea9d · outbound

This paper cites Deep Recurrent Q-Learning for Partially Observable MDPs.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Deep Recurrent Q-Learning for Partially Observable MDPs

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-09T13:35:05.260940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:35:05.260940Z digest=sha256:cffd9d8d89c559fd73e1b9f9c3f0ee2bc7f7383d11b8f5a4d3f047caa55e8954

Observation 38aab464-d00d-4598-bcbb-044fb11c73db · outbound

This paper cites Learning multiagent communication with backpropagation.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Learning multiagent communication with backpropagation

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.445578Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.265128Z digest=sha256:161fd607afcc3f39f96b1335f7bd17b7217f74b42139677673548e388728393d

Observation 52a9748d-de8a-41f0-94bc-5704f8ecb444 · outbound

This paper cites Multi-agent reinforcement learning: A selective overview of theories and algorithms.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Multi-agent reinforcement learning: A selective overview of theories and algorithms

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-09T13:35:05.268404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:35:05.268404Z digest=sha256:4c14c009f8f458b3d07bd00af9282acaa9b58efb028627313f692c94dc05009c

Observation 2d479a2d-6418-4b0b-a995-223270db8e4b · outbound

This paper cites Autonomous agents modelling other agents: A comprehensive survey and open problems.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Autonomous agents modelling other agents: A comprehensive survey and open problems

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.428242Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.272219Z digest=sha256:e9815ea1e56d7628ad89a3098397b0cc78091b2119758b7ad53ae56122bdee0d

Observation 55fbf2b6-b9b4-4876-b08d-3c48abfac25e · outbound

This paper cites Multi-agent reinforcement learning for markov routing games: A new modeling paradigm for dynamic traffic assignment.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Multi-agent reinforcement learning for markov routing games: A new modeling paradigm for dynamic traffic assignment

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.415652Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.276085Z digest=sha256:3a257df6e6d16373efb6af94b843ad9e83806b8243dab0694ec60813c4b8244a

Observation 8fd6ee0a-bcc8-4ab4-babe-c89bc1982c30 · outbound

This paper cites Constrained markov decision processes.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Constrained markov decision processes

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.403965Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.279166Z digest=sha256:8d65e563c90db29d9391743ee851dc5ffda5e0af5908f872d1acdf2ceb761616

Observation c4e3d7dd-f209-463d-ae0c-1b047542219a · outbound

This paper cites Constrained policy optimization.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Constrained policy optimization

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-09T13:35:05.282099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:35:05.282099Z digest=sha256:fd6ed337dd464461bd975430a09a2857e966638ac5c97ae3c574966da613407f

Observation debcc453-b6e5-43ef-8fa7-9648677ddaee · outbound

This paper cites The bargaining problem.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics The bargaining problem

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.387783Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.285682Z digest=sha256:905b1aa039b10398b0f9e410e4a3e904230515e6f75f463248ac444ad1d8adf6

Observation b363d55a-2b61-4d8f-b4cd-ff2b219de95a · outbound

This paper cites Approximately fair allocations of indivisible goods.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Approximately fair allocations of indivisible goods

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.375310Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.288863Z digest=sha256:d14df384020ba57d19e18406d535b5418197d52300d7ef06147afcaecd76e607

Observation 58f6053b-92e5-45bd-b534-f6fa92c9ed6a · outbound

This paper cites Cooperation and fairness in multi-agent reinforcement learning.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Cooperation and fairness in multi-agent reinforcement learning

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:35:05.364735Z

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.

source=arxiv_source observed=2026-08-09T13:35:05.292787Z digest=sha256:d4489779ef1dcc74ddc3af999c3b10227280fb3beeab7a7a679fddb68552590c

Observation 50527953-a7fb-45e4-8dd4-f8aebcd22fb8 · outbound

This paper cites A Theory of Justice.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics A Theory of Justice

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-09T13:35:05.295699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:35:05.295699Z digest=sha256:97b6629fa4934860f27a51ed926538db3be53e902e86fe4bec4bc53f2c8b5453

Observation 68d4383f-881c-43f9-9bfa-3196ab37e5b2 · outbound

This paper cites Proximal Policy Optimization Algorithms.

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics Proximal Policy Optimization Algorithms

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-09T13:35:05.298663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:35:05.298663Z digest=sha256:2964e1c8d46de057a870c9f6ff30b5ef48cd8eef6cabc9058a9e78976e899915

Pith citing papers

Observation b83def27-7114-408e-8823-662509c6511a · inbound

Multi-Year Maintenance Planning for Large-Scale Infrastructure Systems: A Novel Network Deep Q-Learning Approach cites this paper.

Multi-Year Maintenance Planning for Large-Scale Infrastructure Systems: A Novel Network Deep Q-Learning Approach CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-06T14:37:11.811870Z

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.

source=arxiv_source observed=2026-08-06T14:37:10.984263Z digest=sha256:91e5ce397b3fcf52ee0e96d6bcdbc92e953c5440f95e3c68f1340403efbd06f4