Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning | Paper Citation Record

Observation a8e4b05e-3d47-4530-93ff-96c73a64bb6c · outbound

This paper cites Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case

Reference 1

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Observation b848690e-3849-4955-b4f6-17b6eab4551a · outbound

This paper cites Decentralized control of partially observable markov decision processes.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Decentralized control of partially observable markov decision processes

Reference 2

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Observation f292d0a0-250d-4d4a-9974-e9ba44dd67cf · outbound

This paper cites A comprehensive guide to network routing.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A comprehensive guide to network routing

Reference 3

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Observation 00873ab2-cf67-47de-981c-2b22751cc460 · outbound

This paper cites A markovian decision process.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A markovian decision process

Reference 4

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Observation 7102f297-7981-4670-8c9a-c1e69b375dee · outbound

This paper cites Packet routing in dynamically changing networks: A reinforcement learning approach.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Packet routing in dynamically changing networks: A reinforcement learning approach

Reference 5

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Observation 69325b79-e5f6-484b-8518-f6689911a798 · outbound

This paper cites Intelligent routing based on reinforcement learning for software-defined networking.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Intelligent routing based on reinforcement learning for software-defined networking

Reference 6

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Observation 08d1b0a7-b526-43bc-8341-3a75740e2c81 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 7

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Observation a0a99afb-4708-4c2d-bce9-145934c96eaa · outbound

This paper cites Deep reinforcement learning from human preferences.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Deep reinforcement learning from human preferences

Reference 8

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Observation 52d396c7-db28-4be0-81c8-ea747d2bce23 · outbound

This paper cites Multi-agent Reinforcement Learning for Networked System Control.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Multi-agent Reinforcement Learning for Networked System Control

Reference 9

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Observation 5f8da771-163b-4bfe-b87b-9b8728afa4e5 · outbound

This paper cites A deep reinforcement learning-based multi- optimality routing scheme for dynamic iot networks.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A deep reinforcement learning-based multi- optimality routing scheme for dynamic iot networks

Reference 10

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Observation 1c18333a-aa79-49f4-88b4-3001ca8324e6 · outbound

This paper cites Tarmac: Targeted multi-agent communication.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Tarmac: Targeted multi-agent communication

Reference 11

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Observation ab1b8b78-4917-4734-a0b5-8e54b104d983 · outbound

This paper cites Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.010511Z

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Observation bde8f26d-e886-4b7a-be37-e670cae6bc26 · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Convolutional neural networks on graphs with fast localized spectral filtering.Advances in neural information processing systems, 29, 2016

Reference 13

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Observation 72dccdcb-b76b-4d82-a081-96522465c861 · outbound

This paper cites Learning individually inferred communication for multi-agent cooperation.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning individually inferred communication for multi-agent cooperation

Reference 14

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Observation a2787b4e-1471-440b-9a2b-8565c2efe51d · outbound

This paper cites Learning correlated communication topology in multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning correlated communication topology in multi-agent reinforcement learning

Reference 15

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Observation 9df10032-8096-4d38-9331-051ec0cdcd92 · outbound

This paper cites A Review of Cooperation in Multi-agent Learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A Review of Cooperation in Multi-agent Learning

Reference 16

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Observation 8fca9d74-4d64-4847-a067-830f1c4ccb5e · outbound

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

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning to communicate with deep multi-agent reinforcement learning

Reference 17

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Observation e5b6c828-69a3-45c8-b29c-4fdee18c1536 · outbound

This paper cites Stabilising experience replay for deep multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Stabilising experience replay for deep multi-agent reinforcement learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.037684Z

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Observation c7bb6053-10b5-47d1-8e03-ed1d32486303 · outbound

This paper cites Counterfactual multi-agent policy gradients.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Counterfactual multi-agent policy gradients

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.042259Z

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Observation 5e4718cb-7f45-4e1f-8103-1c7b30f56219 · outbound

This paper cites Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

Reference 20

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Observation c367435e-5475-471e-a617-6f69430840a9 · outbound

This paper cites Learning and generating distributed routing protocols using graph-based deep learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning and generating distributed routing protocols using graph-based deep learning

Reference 21

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Observation c10a9f8f-dfcf-4bf2-ae3a-107d7b089731 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Understanding the difficulty of training deep feedforward neural networks

Reference 22

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Observation b5b82f43-8b52-4057-ac39-b5c2f8be437a · outbound

This paper cites Anti-Symmetric DGN: a stable architecture for Deep Graph Networks.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Anti-Symmetric DGN: a stable architecture for Deep Graph Networks

Reference 23

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Observation 17a6a3a7-572c-4486-80f9-9da945e985fe · outbound

This paper cites Model-based sparse communication in multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Model-based sparse communication in multi-agent reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.773547Z

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Observation 064e3636-07eb-420c-a38c-a2e9c701fc39 · outbound

This paper cites Deep recurrent q-learning for partially observable mdps.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Deep recurrent q-learning for partially observable mdps

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.758956Z

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Observation 3d3f85fe-101e-4e08-98f1-09077ac38ca5 · outbound

This paper cites Routing information protocol.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Routing information protocol

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.745415Z

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Observation 51fc9586-3a3e-4c9f-90f4-01767b167e26 · outbound

This paper cites A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity

Reference 27

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Observation bceacc9e-0113-4a9e-8a6d-003c7e0b4bd9 · outbound

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

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A survey and critique of multiagent deep reinforcement learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.730443Z

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Observation 19173d81-38c5-48d7-b652-392d8f2537fa · outbound

This paper cites Learning attentional communication for multi-agent cooperation.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning attentional communication for multi-agent cooperation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.715301Z

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Observation a0f051ea-6c39-4a3b-9071-0635dd4eda75 · outbound

This paper cites Graph Convolutional Reinforcement Learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Graph Convolutional Reinforcement Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.093258Z

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Observation 169d0801-802b-44a9-85a6-ef138e94a04c · outbound

This paper cites Message-dropout: An efficient training method for multi-agent deep reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Message-dropout: An efficient training method for multi-agent deep reinforcement learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.698752Z

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Observation 68f434d0-8528-4923-a0bc-70b3a1fa74d6 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.102161Z

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Observation a6f5efb7-182b-4cfa-ab26-e456d1425aac · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey .IEEE Transactions on Intelligent Transportation Systems, 23(6):4909– 4926, 2021.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Deep reinforcement learning for autonomous driving: A survey .IEEE Transactions on Intelligent Transportation Systems, 23(6):4909– 4926, 2021

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.682658Z

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Observation 33248a28-b17f-4e72-9bd2-4761ebbda4de · outbound

This paper cites Reinforcement learning in robotics: A survey .The International Journal of Robotics Research, 32(11):1238–1274, 2013.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Reinforcement learning in robotics: A survey .The International Journal of Robotics Research, 32(11):1238–1274, 2013

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.667302Z

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Observation b5a6de7b-bf60-4ccc-a213-077719077fab · outbound

This paper cites Actor-critic algorithms.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Actor-critic algorithms

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.651678Z

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Observation c4d22917-2221-40ae-bd8d-d0dcff72c2a6 · outbound

This paper cites An algorithm for distributed reinforcement learning in cooperative multi-agent systems.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning An algorithm for distributed reinforcement learning in cooperative multi-agent systems

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.637334Z

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Observation aef3018b-a927-4565-94fc-c6f2cbf1261c · outbound

This paper cites Continuous control with deep reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Continuous control with deep reinforcement learning

Reference 37

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Observation cc42ed77-90e1-4977-b453-6fc887506141 · outbound

This paper cites Learning to ground multi-agent communication with autoencoders.Advances in Neural Information Processing Systems, 34:15230–15242, 2021.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning to ground multi-agent communication with autoencoders.Advances in Neural Information Processing Systems, 34:15230–15242, 2021

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.622446Z

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Observation 055f5205-2550-419c-a7bc-69790d398054 · outbound

This paper cites Markov games as a framework for multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Markov games as a framework for multi-agent reinforcement learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.608086Z

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Observation 5916d252-8048-449f-bbe9-8850ff036a2b · outbound

This paper cites When2com: Multi- agent perception via communication graph grouping.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning When2com: Multi- agent perception via communication graph grouping

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.594223Z

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Observation 12446ce0-f48f-490e-a32b-fe29563756a3 · outbound

This paper cites Multi- agent game abstraction via graph attention neural network.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Multi- agent game abstraction via graph attention neural network

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.580577Z

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Observation fed748fa-1a17-4104-b82b-1dbe010d6678 · outbound

This paper cites Decoupled Weight Decay Regularization.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Decoupled Weight Decay Regularization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.149128Z

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Observation e46ab52e-eaaf-4656-9fea-e1ca635b6837 · outbound

This paper cites Border Gateway Protocol (BGP).

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Border Gateway Protocol (BGP)

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.565087Z

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Observation b0ab3129-96a4-488d-886a-1e20e290ee93 · outbound

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

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.550906Z

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Observation 62b6ea2c-6a63-4893-aedf-1cadfbf93908 · outbound

This paper cites Rectifier nonlinearities improve neural network acoustic models.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Rectifier nonlinearities improve neural network acoustic models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.536069Z

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Observation 934118c1-5dd1-4414-8bcb-551a19e72630 · outbound

This paper cites Distributed policy evaluation under multiple behavior strategies.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Distributed policy evaluation under multiple behavior strategies

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.520878Z

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Observation b90ae8d7-436d-4868-8c9b-4b3b7051e4b6 · outbound

This paper cites An sdn perspective to mitigate the energy consumption of core networks–g´eant2.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning An sdn perspective to mitigate the energy consumption of core networks–g´eant2

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.505023Z

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Observation 0d25e29f-7c49-48bb-a31a-3a62e47b56d6 · outbound

This paper cites Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Hysteretic q-learning: an algorithm for decentralized reinforcement learning in cooperative multi-agent teams

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.489401Z

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Observation db165626-55e7-4c05-91b8-894b99a0264b · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.178466Z

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Observation 1b062434-a96f-45db-b7bf-b5798f19d1e0 · outbound

This paper cites Multi- agent deep learning for simultaneous optimization for time and energy in distributed routing system.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Multi- agent deep learning for simultaneous optimization for time and energy in distributed routing system

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.473959Z

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Observation 64a517a9-cacb-414f-b124-57c4e9159f5f · outbound

This paper cites The complexity of optimal small policies.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning The complexity of optimal small policies

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.459025Z

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Observation 0c82be9f-b531-4a46-9f6e-c64e09b90afc · outbound

This paper cites Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.191960Z

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Observation a8654248-452a-4490-bc2b-693ede7fce15 · outbound

This paper cites an unresolved cited work.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Unresolved cited work

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.196746Z

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Observation 44149666-9d4d-4f3a-8a5f-7a574ebbddd5 · outbound

This paper cites Magic: Multi-agent graph-attention communication.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Magic: Multi-agent graph-attention communication

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.444269Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e18b11af-b065-40f8-90b3-093c52edd20b · outbound

This paper cites Tree-based solution methods for multiagent pomdps with delayed communication.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Tree-based solution methods for multiagent pomdps with delayed communication

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.429498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0c0c5910-7727-4b67-a10e-e9bbac1e1773 · outbound

This paper cites A review of cooperative multi-agent deep reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A review of cooperative multi-agent deep reinforcement learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.415263Z

Source-reported events for the cited work

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Observation 734c7894-32da-473f-a3f1-0ebae3fae2ed · outbound

This paper cites Reinforcement learning algorithm for non-stationary environments.Applied Intelligence, 50(11):3590– 3606, 2020.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Reinforcement learning algorithm for non-stationary environments.Applied Intelligence, 50(11):3590– 3606, 2020

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.400718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 24c5df1a-8303-4637-9a0d-6b9ca01289ca · outbound

This paper cites Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.220165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d3e9928-dd92-42a3-8b3c-cfa2c3dd72b8 · outbound

This paper cites Markov decision processes: discrete stochastic dynamic programming.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Markov decision processes: discrete stochastic dynamic programming

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.386286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2d7f95bb-c95c-456c-bc9c-2f98db904614 · outbound

This paper cites Monotonic value function factorisation for deep multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Monotonic value function factorisation for deep multi-agent reinforcement learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.371170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 929af3e0-f805-4262-b087-96633f8b376b · outbound

This paper cites The graph neural network model.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning The graph neural network model

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.356302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 24f182d9-21bf-465b-a632-10751b784a91 · outbound

This paper cites Distributed online service coordination using deep reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Distributed online service coordination using deep reinforcement learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.341484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e10c11e5-7c8c-4928-9058-87ca849c2c0a · outbound

This paper cites Trust region policy optimization.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Trust region policy optimization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.326988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 342f4641-78e5-4a33-91f2-b114d6bcfbe1 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.247433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a022650-2649-4b8e-9643-de3bca80f10d · outbound

This paper cites Structured sequence modeling with graph convolutional recurrent networks.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Structured sequence modeling with graph convolutional recurrent networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.312441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 81e0e62d-b99d-4ee4-8b83-361d32223f6d · outbound

This paper cites Software-defined networking (sdn): A reference architecture and open apis.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Software-defined networking (sdn): A reference architecture and open apis

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.297284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5c3286a0-fdea-4427-b09c-973db1a885b9 · outbound

This paper cites Open shortest path first (ospf) routing protocol simulation.ACM SIGCOMM Computer Communication Review, 23(4):53–62, 1993.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Open shortest path first (ospf) routing protocol simulation.ACM SIGCOMM Computer Communication Review, 23(4):53–62, 1993

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.281633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9e2a8687-8c28-4c72-9b5d-00ab8f6040f3 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Mastering the game of go with deep neural networks and tree search

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.266269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2e1c5d4-bd6e-4265-93e1-be38b11fb6cd · outbound

This paper cites The behavior of organisms: An experimental analysis.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning The behavior of organisms: An experimental analysis

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.251476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4a80ce4a-ef93-4180-a452-92db04dfeaa6 · outbound

This paper cites Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.237000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 89b45f03-85f4-4e75-846b-bb842941004d · outbound

This paper cites Multi-agent temporal-difference learning with linear function approximation: Weak convergence under time-varying network topologies.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Multi-agent temporal-difference learning with linear function approximation: Weak convergence under time-varying network topologies

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.205297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 56ace35b-e1d8-49d8-829f-d8d14d6faf5d · outbound

This paper cites Phase transitions and critical phenomena , volume 7.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Phase transitions and critical phenomena , volume 7

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.190401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ecb1d65-0663-4ae4-80d4-85c1dd0b6698 · outbound

This paper cites Decentralized Policy Optimization.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Decentralized Policy Optimization

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.294489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecdb2a53-d8f6-40e9-8bfb-eaf0aad2f13d · outbound

This paper cites A general formulation of independent policy optimization in fully decentralized marl.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning A general formulation of independent policy optimization in fully decentralized marl

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.176309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6d63df4-eb55-4646-a2c5-5aebb875efa5 · outbound

This paper cites Learning multiagent communication with backpropagation.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning multiagent communication with backpropagation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.161850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dc6d53f6-8f8d-4b9b-bfc8-bf91e9256274 · outbound

This paper cites Value-Decomposition Networks For Cooperative Multi-Agent Learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Value-Decomposition Networks For Cooperative Multi-Agent Learning

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.308211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e405b032-81b0-42dc-bd14-fe1edba86e07 · outbound

This paper cites Learning to predict by the methods of temporal differences.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning to predict by the methods of temporal differences

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.147366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2001a644-55c6-4061-8c17-bc17ebed092f · outbound

This paper cites Reinforcement learning: An introduction.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Reinforcement learning: An introduction

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.132584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bc7465da-7d64-469d-aec6-10acd90c907c · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Policy gradient methods for reinforcement learning with function approximation

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.117927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 43f6bd8e-d1cd-4999-89e8-f4fba2835886 · outbound

This paper cites Attention is all you need.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Attention is all you need

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.102917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b12d16cb-99ff-42d8-8930-9c9ab9918e40 · outbound

This paper cites Graph Attention Networks.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Graph Attention Networks

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.330494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a44ae58-9373-41ef-aede-9d78a03c3981 · outbound

This paper cites QPLEX: Duplex Dueling Multi-Agent Q-Learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning QPLEX: Duplex Dueling Multi-Agent Q-Learning

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.335088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b81c1452-5db4-4a2a-a318-c76b04ab6606 · outbound

This paper cites AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:04:02.475872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4acaf67d-26f6-4fb5-88ba-bbe840787b42 · outbound

This paper cites Learning from delayed rewards.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Learning from delayed rewards

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.087803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8f715a44-4264-48d4-b9e8-ccb0903fdf25 · outbound

This paper cites Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.348347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1004d036-1a94-4167-a43f-619c514d3b24 · outbound

This paper cites Mambpo: Sample- efficient multi-robot reinforcement learning using learned world models.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Mambpo: Sample- efficient multi-robot reinforcement learning using learned world models

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.072713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c85d29e8-f2d3-4842-8ada-616f180010ca · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.056741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c558a1a2-b6db-4a16-8794-5c0b06b90a4d · outbound

This paper cites Distributed average consensus with least-mean-square deviation.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Distributed average consensus with least-mean-square deviation

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.041135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b50db915-bd6a-4caf-bd02-c92a48ac364b · outbound

This paper cites Mean field multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Mean field multi-agent reinforcement learning

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.025457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0f5fb461-6ba9-484e-811f-6208202d90c5 · outbound

This paper cites Toward packet routing with fully distributed multiagent deep reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Toward packet routing with fully distributed multiagent deep reinforcement learning

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:03.009167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 94d3e610-6c29-4175-85d6-9d36fb533e71 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning The surprising effectiveness of ppo in cooperative multi-agent games

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:02.992836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a0d22916-023a-49d2-ae23-165b655e0e06 · outbound

This paper cites Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-10T23:04:02.380740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cc70e94-6f38-4545-84b4-64cad5839837 · outbound

This paper cites Fully decentralized multi-agent reinforcement learning with networked agents.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Fully decentralized multi-agent reinforcement learning with networked agents

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:02.977696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5920b819-cf2f-4cdf-ba83-111f255b75ab · outbound

This paper cites Decentralized multi-agent reinforcement learning with networked agents: Recent advances.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Decentralized multi-agent reinforcement learning with networked agents: Recent advances

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:02.962835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3ba6dbf6-e9b1-4e5a-8957-f02418e16ed6 · outbound

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

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning Multi-agent reinforcement learning: A selective overview of theories and algorithms

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:02.947232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 29e8da4b-121a-42bd-aba7-ad440f4fa09a · outbound

This paper cites High- speed ramp merging behavior decision for autonomous vehicles based on multi-agent reinforcement learning.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning High- speed ramp merging behavior decision for autonomous vehicles based on multi-agent reinforcement learning

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:04:02.930577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c79fcaf0-ccbd-4626-ba2c-4bd1484e0f33 · outbound

This paper cites pages 16.

Dynamic Graph Communication for Decentralised Multi-Agent Reinforcement Learning pages 16

Reference 5896

Resolution: parse uncertain; raw_fallback, observed 2026-08-10T23:04:03.220806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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